literature review data analytics

Open Access is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new Open Access publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers.

We are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including Nobel Prize winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too.

Brief introduction to this section that descibes Open Access especially from an IntechOpen perspective

Want to get in touch? Contact our London head office or media team here

Our team is growing all the time, so we’re always on the lookout for smart people who want to help us reshape the world of scientific publishing.

Home > Books > Social Media and Machine Learning

Literature Review on Big Data Analytics Methods

Submitted: 18 February 2019 Reviewed: 14 May 2019 Published: 24 October 2019

DOI: 10.5772/intechopen.86843

Cite this chapter

There are two ways to cite this chapter:

From the Edited Volume

Social Media and Machine Learning

Edited by Alberto Cano

To purchase hard copies of this book, please contact the representative in India: CBS Publishers & Distributors Pvt. Ltd. www.cbspd.com | [email protected]

Chapter metrics overview

1,876 Chapter Downloads

Impact of this chapter

Total Chapter Downloads on intechopen.com

Total Chapter Views on intechopen.com

Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed.

• big data analytics
• machine learning
• deep learning

Author Information

Iman raeesi vanani.

• Information Technology Management, Allameh Tabataba’i University, Iran

Setareh Majidian *

*Address all correspondence to: [email protected]

1. Introduction

Digital era with its opportunity and complexity overwhelms industries and markets that are faced with a huge amount of potential information in each transaction. Being aware of the value of gathered data and benefitting from hidden knowledge create a new paradigm in this era, which redefines the meaning of power for corporation. The power of information leads organizations toward being agile and to hit the goals. Big data analytics (BDA) enforces industries to describe, diagnose, predict, prescribe, and cognate the hidden growth opportunities and leads them toward gaining business value [ 68 ]. BDA deploys advanced analytical techniques to create knowledge from exponentially increasing amount of data, which will affect the decision-making process in decreasing complexity of the process [ 43 ]. BDA needs novel and sophisticated algorithms that process and analyze real-time data and result in high-accuracy analytics. Machine and deep learning allocate their complex algorithms in this process considering the problem approach [ 28 ].

In this research, a literature review on big data analytics, deep learning and its algorithms, and machine learning and related methods has been considered. As a result, a conceptual model is provided to show the relation of the algorithms that helps researchers and practitioners in deploying BDA on IOT data.

The process of discussing over DL and ML methods has been shown in Figure 1 .

The big data analytics methods in this research.

2. Big data and big data analytics

One of the vital consequences of the digital world is creating a collection of bulk of raw data. Managing such valuable capital with different shape and size on the basis of organizations’ needs the manager’s attention. Big data has the power to affect all parts of society from social aspect to education and all in between. As the amount of data increases especially in technology-based companies, the matter of managing raw data becomes much more important. Facing with features of raw data like variety, velocity, and volume of big data entitles advanced tools to overcome the complexity and hidden body of them. So, big data analytics has been proposed for “experimentation,” “simulations,” “data analysis,” and “monitoring.” Machine learning as one of the BDA tools creates a ground to have predictive analysis on the basis of supervised and unsupervised data input. In fact, a reciprocal relation has existed between the power of machine learning analytics and data input; the more exact and accurate data input, the more effective the analytical performance. Also, deep learning as a subfield of machine learning is deployed to extract knowledge from hidden trends of data [ 28 ].

3. Big data analytics

In digital era with growing rate of data production, big data has been introduced, which is known by big volume, variety, veracity, velocity, and high value. It brings hardness in analyzing with itself which entitled organization to deploy a new approach and tools in analytical aspects to overcome the complexity and massiveness of different types of data (structured, semistructured, and unstructured). So, a sophisticated technique that aims to cope with complexity of big data by analyzing a huge volume of data is known as big data analytics [ 50 ]. Big data analytics for the first time was coined by Chen Chiang (2012) who pointed out the relation between business intelligence and analytics that has strong ties with data mining and statistical analysis [ 11 ].

Big data analytics supports organizations in innovation, productivity, and competition [ 16 ]. Big data analytics has been defined as techniques that are deployed to uncover hidden patterns and bring insight into interesting relations in understanding contexts by examining, processing, discovering, and exhibiting the result [ 69 ]. Complexity reduction and handling cognitive burden in knowledge-based society create a path toward gaining advantages of big data analytics. Also, the most vital feature that led big data analytics toward success is feature identification. This means that the crucial features that have important affection on results should be defined. It is followed by identifying of corelations between input and a dynamic given point, which may change during times [ 69 ].

As a result of fast evolution of big data analytics, e-business and dense connectivity globally have flourished. Governments, also, take advantages of big data analytics to serve better services to their citizens [ 69 ].

Big data in business context can be managed and analyzed through big data analytics, which is known as a specific application of this field. Also, big data gained from social media can be managed efficiently through big data analytics process. In this way, customer behavior can be understood and five features of big data, which are enumerated as volume, velocity, value, variety, and veracity, can be handled. Big data analytics not only helps business to create a comprehensive view toward consumer behavior but also helps organizations to be more innovative and effective in deploying strategies [ 14 ]. Small and medium size company use big data analytics to mine their semistructured big data, which results in better quality of product recommendation systems and improved website design [ 19 ]. As Ref. [ 9 ] cited, big data analytics gains advantages of deploying technology and techniques on their massive data to improve a firm’s performance.

According to Ref. [ 19 ], the importance of big data analytics has been laid in the fact that decision-making process is supported by insight, which is the result of processing diverse data. This will turn decision-making process into an evidence-based field. Insight extraction from big data has been divided into two main processes, namely data management and data analytics with the former referring to technology support for gathering, storing, and preparing data for analyzing purpose and the latter is about techniques deployed for data analyzing and extracting knowledge from them. Thus, big data analytics has been known as a subprocess of insight extraction. Big data analytics tools are text analytics, audio analytics, video analytics, social media analytics, and predictive analytics. It can be inferred that big data analytics is the main tool for analyzing and interpreting all kinds of digital information [ 35 ]. And the processes involved are data storage, data management, data analyzing, and data visualization [ 9 ].

Big data analytics has the potential for creating effective and efficient value in both operational and strategic approach for organization and it plays as a game changer in augmenting productivity [ 20 ].

Industry practitioners believe that big data analytics is the next ‘blue ocean’ that brings opportunities for organizations [ 33 ], and it is known as “the fourth paradigm of science” [ 70 ].

Fields of machine learning (ML) and deep learning (DL) were expanded to deal with BDA. Different fields like “medicine,” “Internet of Things (IOT),” and “search engines” deploy ML for exploration of predictive features of big data. In other words, it generalizes learnt patterns to predict future data. Feature construction and data representation are two main elements of ML. Also, useful data extraction from big data is the reason for deploying DL, which is a human-brain inspired technique for processing neural signals as a subfield of ML [ 28 ].

4. Big data analytics and deep learning

In 1940s, deep learning was been introduced [ 71 ], but the birth of deep learning algorithms has been determined in year 2006 when layer-wise-greedy-learning method was introduced by Hinton to overcome the deficiency of neural network (NN) method in finding optimized point by trapping in optima local point that is exacerbated when the size of training data was not enough. The underlying thought of proposed method by Hinton is to use unsupervised learning before layer-by-layer training happens [ 72 ].

Inspiring from hierarchical structure of human brain, deep learning algorithms extract complex hidden features with a high level of abstraction. When massive amounts of unstructured data represent, the layered architecture of deep learning algorithms works effectively. The goal of deep learning is to deploy multiple transformation layers where in every layer output representation is occurred [ 42 ]. Big data analytics comprises the whole learnt untapped knowledge gained from deep learning. The main feature of big data analytics, which is extracting underlying features in huge amounts of data, makes it a beneficial tool for big data analytics [ 42 ].

convolutional neural networks (CNN)

restricted Boltzmann machines

autoencoder

sparse coding [ 24 ]

4.1 Convolutional neural networks (CNN)

CNN inspired from neural network model as a type of deep learning algorithm has a “convolutional layer” and “subsampling layer” architecture. Multi-instance data is deployed as a bag of instances in which each data point is a set of instances [ 73 ].

CNN has been known with three features namely “local field,” “subsampling,” and “weight sharing” and comprised of three layers, which are input, hidden that consists of “convolutional layer” and “subsampling layer” and output layer. In hidden layer, each “convolutional layer” comes after “subsampling layer.” CNN training process has been done in two phases of “feed forward” in which the result of previous level entered into next level and “back propagation” pass, which is about modification of errors and deviation through a process of spreading training errors backward and in a hierarchical process [ 74 ]. In the first layer, convolution operation is deployed that is to take various filtering phases in each instances, and then, nonlinear transformation function takes place as the result of previous phase transforming into a nonlinear space. After that, the transformed nonlinear space is considered in max-pooling layer, which represents the bag of instances. This step has been done by considering the maximum response of each instance, which was in filtering step. The representation creates a strong pie with the maximum response that can be deployed by predicting instances’ status in each class. This will lead to constructing a classification model [ 73 ].

CNN is comprised of feature identifier, which is an automatic learning process from extracted features from data with two components of convolutional and pooling layers. Another element of CNN is multilayer perception, which is about taking features that were learned into classification phase [ 3 ].

4.2 Deep neural network (DNN)

A deep architecture in supervised data has been introduced with advances in computation algorithm and method, which is called deep neural network (DNN) [ 3 ]. It originates from shallow artificial neural networks (SANN) that are related to artificial intelligence (AI) [ 30 ].

As hierarchical architecture of DL can constitute nonlinear information in the set of layers, DNN deploys a layered architecture with complex function to deal with complexity and high number of layers [ 3 ].

DNN is known as one of the most prominent tools for classifying [ 49 ] because of its outstanding classification performance in complex classification matters. One of the most challenging issues in DNN is training performance of it, as in optimization problems it tries to minimize an objective function with high amount of parameters in a multidimensional searching space. So, fining and training a proper DNN optimization algorithm requires in high level of attention. DNN is constructed of structure stacked denoising auto encoder (SDAE) [ 75 ] and has a number of cascade auto encoder layers and softmax classifier. The first one deploys raw data to generate novel features, and with the help of softmax, the process of feature classification is performed in an accurate way. The cited features are complementary to each other that helps DNN do its main performance, which is classification in an effective way. Gradient descent (GD) algorithm, which is an optimization method, can be deployed in linear problems with no complex objective function especially in DNN training, and the main condition of this procedure is that the amount of optimization parameter is near to optimal solution [ 6 ]. According to Ref. [ 30 ], DNN with the feature of deep architecture is deployed as a prediction model [ 30 ].

4.3 Recurrent neural network (RNN)

RNN, a network of nodes that are similar to neurons, was developed in 1980s. Each neuron-like node is interconnected with each other, and it can be divided into categories of input, hidden, and output neurons. The data will receive, transform, and generate results in this triple process. Each neuron has the feature of time-varying real-valued activation and every synapse is real-valued weight justifiable [ 66 ]. A classifier for neural networks has outstanding performance in not only learning and approximating [ 105 ] but also in dynamic system modeling with nonlinear approach by using present data [ 29 , 52 ]. RNN with the background of human brain–inspired algorithm has been derived from artificial neural network but they are slightly different from each other. Various fields of “associative memories,” “image processing,” “pattern recognition,” “signal processing,” “robotics,” and “control” have been in the center of focus in research of RNN [ 67 ]. RNN with its feedback and feed forward relations can take a comprehensive view from past information and deploy it for adjusting with sudden changes. Also, RNN has the capability of using time-varying data in a recursive way, which simplified the neural network architecture. Its simplicity and dynamic features work effectively in real-time problems [ 40 ]. RNN has the ability to process temporal data in hierarchy method and take multilayer of abstract data to show dynamical features, which is another capability of RNN [ 18 ]. RNN has the potential to make connection between signals in different levels, which brings significant processing power with huge amounts of memory space [ 45 ].

5. Big data analytics and machine learning

Machine learning has been defined as predictive algorithms by data interpretation, which is followed by learning algorithm in an unstructured program. Three main categories of ML are supervised, unsupervised, and reinforcement learning [ 47 ], which is done during “data preprocessing,” “learning,” and “evaluation phase.” Preprocessing is related to transformation of raw data into right form that can be deployed in learning phase, which comprises of some levels like cleaning the data, extracting, transforming, and combining it. In the evaluation phase, data set will be selected, and evaluation of performance, statistical tests, and estimation of errors or deviation occur. This may lead to modifying selected parameters from learning process [ 76 ]. The first one refers to analyzing features that are critical for classification through a given training data. The data deployed in training algorithm will then become trained and then it will be used in testing of unlabeled data. After interpreting unlabeled data, the output will be generated, which can be classified as discrete or regression if it is continuous. On the other hand, ML can be deployed in pattern identification without training process, which is called unsupervised ML. In this category, when pattern of characteristics are used to group the data, cluster analysis is formed, and if the hidden rules of data have been recognized, another form of ML, which is association, will be formed [ 77 ]. In the other words, the main process of unsupervised ML or clustering is to find natural grouping from those data, which is unlabeled. In this process, K cluster in a set number of data is much more similar in comparison with other clusters considering similarity measure. Three categories of unsupervised ML are “hierarchical,” “partitioned,” and “overlapping” techniques. “Agglomerative” and “divisive” are two kinds of hierarchical methods. The first one is referred to an element that creates a separate cluster with tendency to get involved with larger cluster; however, the second one is a comprehensive set that is going to divide into some smaller clusters. “Partitioned” methods begin with creating several disjoint clusters from data set without considering any hierarchical structure, and “overlapping” techniques are defined as methods that try to find fuzzy or deffuzy partitioning, which is done by “relaxing the mutually disjoint constraint.” Among all unsupervised learning techniques, K-means grabs attention. “Simplicity” and “effectiveness” are two main characteristics of unsupervised techniques [ 47 ].

5.1 Machine learning and fuzzy logic

Fuzzy logic proposed by Lotfi Zadeh (1965) has been deployed in many fields from engineering to data analysis and all in between. Machine learning also gains advantage from fuzzy logic as fuzzy takes inductive inference. The changes happened in such grounds like “fuzzy rule induction,” “fuzzy decision trees,” “fuzzy nearest neighbor estimation,” or “fuzzy support vector machines” [ 27 ].

5.2 Machine learning and classification methods

One of the most critical aspects of ML is classifications [ 23 ], which is the initial phase in data analytics [ 17 ]. Prior studies found new fields that can deploy this aspect like face recognition or even recognition of hand writing. According to [ 23 ], operating algorithm of classification has been divided into two categories: offline and online. In offline approach, static dataset is deployed for training. The training process will be stopped by classifiers after training process is finished and modification of data structured will not be allowed. On the other hand, online category is defined as a “one-pass” type, which is learning from new data. The prominent features of data will be stored in memory and will be kept until the processed training data is erased. Incremental and evolving processes (changing data pattern in unstable environment, which is a result of evolutionary system structure, and continuously updating meta-parameters) are two main approaches for online category [ 23 ].

Support vector machine (SVM) was proposed in 1995 by Cortes and Vapnik to solve problems related to multidimensional classification and regression issues as its outstanding learning performance [ 64 ]. In this process, SVM constructs a high-dimensional hyperplane that divides data into binary categories, and finding greatest margin in binary categories considering the hyperplane space is the main objective of this method [ 10 ]. “Statistical learning theory,” “Vapnik-Chervonenkis (VC) dimension,” and the “kernel method” are underlying factors of development of SVM [ 78 ], which deploys limited number of learning patterns to desirable generalization considering a risk minimization structure [ 22 ].

It is highly dependent on the value of K parameter, which is a gauge for determination of neighborhood space.

The method lacks discrimination ability to differentiate between far and close neighbors.

Overlapping or noise may happen when neighbor are close [ 80 ].

KNN as one of the most important data mining algorithms was first introduced for classification problems, which are expanded to pattern recognition and machine learning research. Expert systems take advantage of KNN classification problems. Three main KNN classifiers that put focus on k-nearest vector neighbor in every class of test sample are as follows:

“Local mean-based k-nearest neighbor classifier (LMKNN)”: despite the fact that existing outlier negative influence can be solved by this method, LMKNN is prone to misclassification because of taking single value of k considering neighborhood size per class and applying it in all classes.

“Local mean-based pseudo nearest neighbor classifier (LMPNN)”: LMKNN and PNN methods create LMPNN, which is known as a good classifier in “multi-local mean vectors of k-nearest neighbors and pseudo nearest neighbor based on the multi-local mean vectors for each class.” Outlier points in addition to k sensitivity have been more considered in this technique. However, differentiation of information in nearest sample of classification cannot recognize widely as weight of all classes are the same [ 81 ].

“Multi-local means-based k-harmonic nearest neighbor classifier (MLMKHNN)”: MLMKHNN as an extension to KNN takes harmonic mean distance for classification of decision rule. It deploys multi-local mean vectors of k-nearest neighbors per class of every query sample and harmonic mean distance will be deployed as the result of this phase [ 82 ]. These methods are designed in order to find different classification decisions [ 81 ].

In 2006, Huang et al. proposed extreme learning machine (ELM) as a classification method that works by a hidden single layer feedback in neural network [ 92 ]. In this layer, the input weight and deviation will be randomly generated and least square method will be deployed to determine output weight analytically [ 17 ], which differentiates this method from traditional methods. In this phase, learning happens followed by finding transformation matrix [ 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 ]. It is deployed to minimize the sum-of-squares error function. The result of minimizing function will then be used in classification or reduction of dimension [ 48 ]. Neural networks are divided into two categories of feed forward neural network and feedback neural networks and ELM is on the first category, which has a strong learning ability specially in solving nonlinear functions with high complexity. ELM uses this feature in addition to fast learning methods to solve traditional feed forward neural network problems in a mathematical change without iteration with higher speed in comparison with traditional neural network [ 13 ].

Despite the efficiency of ELM in classification problems, binary classification problems emerge as the deficiency of ELM; as in these problems, a parallel training phase on ELM is needed. In twin extreme learning machine (TELM), the problems will be solved by a simultaneous train and two nonparallel classification hyperplanes, which are deployed for classification. Every hyperplane enters into a minimization function to minimize the distance of it with one class, which is located far away from other classes [ 60 ]. ELM is at the center of attention in data stream classification research [ 83 ].

5.3 Machine learning and clustering

Clustering as a supervised learning method aims to create groups of clusters, which members of it are in common with each other in characteristics and dissimilar with other cluster members [ 84 ]. The calculated interpoint distance of every observation in a cluster is small in comparison with its distance to a point in other clusters [ 36 ]. “Exploratory pattern-analysis,” “grouping,” “decision-making,” and “machine-learning situations” are some main applications of clustering technique. Five groups of clustering are “hierarchical clustering,” “partitioning clustering,” “density-based clustering,” “grid-based clustering,” and “model-based clustering” [ 84 ]. Clustering problems are divided into two categories: generative and discriminative approaches. The first one refers to maximizing the probability of sample generation, which is used in learning from generated models, and the other is related to deploying pairwise similarities, which maximize intercluster similarities and minimize similarities of clusters in between [ 63 ].

There are important clustering methods like K-means clustering, kernel K means, spectral clustering, and density-based clustering algorithms that are at the center of research topics for several decades. In K-means clustering, data is assigned to the nearest center, which results from being unable to detect nonspherical clusters. Kernel k-means and spectral clustering create a link between the data and feature space and after that k-means clustering is deployed. Obtaining feature space is done by using kernel function and graph model by kernel k-means and spectral clustering, respectively. Also spectral clustering deploys Eigen-decomposition techniques additionally [ 26 ]. K-means clustering works effectively in clustering of numerical data, which is multidimensional [ 85 ].

Density-based clustering is represented by DBSCAN, and clusters tend to be separate from data set and be as higher density area. This method does not deploy one cluster for clusters recognition in the data a priori. It considers user-defined parameter to create clusters, which has a bit deviation from cited parameter in clustering process [ 84 ].

5.4 Machine learning and evolutionary methods

The main goal of optimization problems is to find an optimal solution among a set of alternatives. Providing the best solution has become difficult if the searching area is large. Heuristic algorithm proposed different techniques to find the optimal solution, but they lack finding the best solution. However, population-based algorithm was generated to overcome the cited deficiency, which is considered to find the best alternative [ 7 ].

5.5 Genetic algorithms (GA)

GA is defined as a randomized search, which tries to find near-optimal solution in complex and high-dimensional environment. In GA, a bunch of genes that are called chromosomes are the main parameters in the technique. These chromosomes are deployed as a search space. A number of chromosomes that seem as a collection are called population. The creation of a random population will be followed by representing the goodness degree of objective and fitness function related to each string. The result of this step that will be a few of selected string with a number of copies will be entered into the mating pool. By deploying cross-over and mutation process, a new generation of string will be created from the string. This process will be continued until a termination condition is found. “Image processing,” “neural network,” and “machine learning” are some examples of application fields for genetic algorithms [ 38 ]. GA as nature-inspired algorithm is based on genetic and natural selection algorithms [ 31 ].

GA tries to find optimal solution without considering the starting point [ 104 ]; also, GA has the potential to find optimal clustering considering clustering metrics [ 38 ]. Filter and wrapper search are two main approaches of GA in the field of feature selection. The first one aims to investigate the value of features by deploying heuristic-based data characteristics like correlation, and the second one assesses the goodness of GA solution by using machine learning algorithm [ 53 ]. In K-means algorithm, optimized local point is found on the basis of initializing seed values and the generated cluster is on the basis of initial seed values. GA by the aim of finding near-optimal or optimal clustering searches for initial seed values, outperforms K-mean algorithm, and covers the lack of K-mean algorithm [ 4 ]. Gaining knowledge from data base is another ground for GA, which plays the role of building “classifier system” and “mining association rules” [ 58 ].

Feature selection is a vital problem in big data as it usually contains many features that describe target concepts and chooses proper amount of feature for pre-processing traditionally as a main matter was done by data mining. Feature selection is divided into two groups: independent of learning algorithm, which deploys filter approach, and dependent on learning algorithm, which uses a wrapper approach. However, filter approach is independent of learning algorithm, and the optimal set of feature may be dependent on learning algorithm, which is one of the main drawbacks of filter selection. In contrast, wrapper approach by deploying learning algorithm in evaluation of every feature set works better. A main problem of this approach is complexity in computation field, which is overcome by using GA in feature selection as learning algorithm [ 56 ].

5.6 Ant colony optimization (ACO)

Ant colony optimization method was proposed by Dorigo [ 17 ] as a population-based stochastic method [ 15 ]. The method has been created biologically from real ant behavior in food-seeking pattern. In other words, this bionic algorithm has been deployed for finding the optimal path [ 44 ]. The process is that when ants start to seek food they deposit a chemical material on the ground, which is known as pheromone while they are moving toward food source. As the path between the food source and nest become shorter, the amount of pheromone will become larger. New ants in this system tend to choose the path with greater amount of pheromone. By passing time, all ants follow the positive feedback and choose the shortest path, which is signed by greatest amount of pheromone [ 86 ]. The applications of ant colony optimization in recent research have been declared as traveling salesman problem, scheduling, structural and concrete engineering, digital image processing, electrical engineering, clustering, routing optimization algorithm [ 41 ], data mining [ 32 ], robot path planning [ 87 ], and deep learning [ 39 ].

Less complexity in integration of this method with other algorithms

Gain advantage of distributed parallel computing (e.g., intelligent search)

Work better in optimization in comparison with swarm intelligence

High speed and high accuracy

Robustness in finding a quasi-optimal solution [ 41 ]

As it is stated, the emitted material called pheromone causes clustering between species around optimal position. In big data analytics, ant colony clustering is deployed on the grid board to cluster the data objects [ 21 ].

Initializing pheromone trail

Deploying pheromone trail to construct solution

Updating trail pheromone

On the basis of probabilistic state transition rule, which depends on the state of the pheromone, a complete solution is made by each ant. Two steps of evaporation and reinforcement phase are passed in pheromone updating procedure, where evaporation of pheromone fraction happens and emitting of pheromone that shows the level of solution fitness is determined, respectively, which is followed by finalizing condition [ 46 ].

Ant colony decision tree (ACDT) is a branch of ant colony decision that aims to develop decision tress that are created in running algorithm, but as a nondeterministic algorithm in every execution, different decision tree is created. A pheromone trail on the edge and heuristics used in classical algorithm is the principle of ACDT algorithm.

The multilayered ant colony algorithm has been proposed after the disability of one layer ant colony optimization has been declared in finding optimal solution. As an item, value with massive amount of quantity takes too long to grow. In this way, through transactions, maximum quantities of an item is determined and a rough set of membership function will be set, which will be improved by refining process at subsequent levels by reduction in search space. As a result, search ranges will be differing considering the levels. Solution derived from every level is an input for next level, which is considered in the cited approach but with a smaller search space that is necessary for modifying membership functions [ 88 ]. Tsang and Kwong proposed ant colony clustering in anomaly detection [ 65 ].

5.7 Bee colony optimization (BCO)

BCO algorithm works on inspiration from honey bee’s behavior, which is widely used in optimization problems like “traveling salesman problem,” “internet hosting center,” vehicle routing, and the list goes on. Karaboga in 2005 proposed artificial bee colony (ABC) algorithm. The main features of artificial bee colony (ABC) algorithm are simplicity, easy used and has few elements which need to be controlled in optimization problems. “Face recognition,” “high-dimensional gene expression,” and “speech segment classification” are some examples that ABC and ACO use to select features and optimize them by having a big search space. In ABC algorithms, three types of bees called “employed bees (EBees),” “onlooker bees (OBees),” and “scout bees deployed” are deployed. In this process, food sources are positioned and then EBees, where their numbers are equal to number of food source, pass the nectar information to OBees. They are equal to the number of EBees. The information is taken to exploit the food source till the finishing amount. Scouts in exhausted food source are employed to search for new food source. The nectar amount is a factor that shows solution quality [ 25 , 55 ].

This method is comprised of two steps: step forward, which is exploring new information by bees, and step back, which is related to sharing information considering new alternative by bee of hives.

In this method, exploration is started by a bee that tries to discover a full path for its travel. When it leaves the hive, it comes across with random dances of other bees, which are equipped with movement array of other bees that is known as “preferred path.” This will lead in foraging process and it comprises of a full path, which was previously discovered by its partner who guides the bee to the final destination. The process of moving from one node to another will be continued till the final destination is reached. For choosing the node by bees, a heuristic algorithm is used, which involves two factors of arc fitness and the distance heuristic. The shortest distance has the possibility to be selected by bees [ 7 ]. In BCO algorithm, two values of alpha and beta will be considered, which are exploitation and exploration processes, respectively [ 8 ].

5.8 Particle swarm optimization (PSO)

PSO was generated from inspiration from biological organisms, particularly the ability of a grouped animal to work together in order to find the desired location in particular area. The method was introduced by Kennedy and Eberhart in 1995 as a stochastic population-based algorithm, which is known by features like trying to find global optimize point and easy implementation with taking a small amount of parameters in adjusting process. It takes benefit from a very productive searching algorithm, which makes it a best tool to work on different optimization research area and problems [ 59 ].

The searching process is led toward solving a nonlinear optimization problem in a real value search space. In this process, an iterative searching happens to find the destination, which is the optimal point. In other words, each particle has a multidimensional search with a specific space, which is updated by particle experience or the best neighbor’s space and the objective function assesses the fitness value of each particle. The best solution, which is found in each iteration, will be kept in memory. If the optimal solution is found by particle, it is called local best or pbest and the optimal point among the particle neighbors is called global best or gbest [ 89 ]. In this algorithm, every potential solution is considered as a particle, which has several features like the current position and velocity. The balance between global and local search can be adjusted by adopting different inertia weight. One of critical success factors in PSO is a trade-off between global and local search in iteration [ 59 ]. Artificial neural network, pattern classification, and fuzzy control are some area for deploying PSO [ 5 ]. Social interaction and communication metaphor like “birds flock and fish schooling” developed this algorithm and it works on the basis of improving social information sharing, which is done among swarm particles [ 12 ].

5.9 Firefly algorithm (FA)

Firefly algorithm was been introduced by Yang [ 16 ]. The main idea of FA is that each firefly has been assumed as unisexual, which is attracted toward other firefly regardless of the gender. Brightness is the main attraction for firefly that stimulates the less bright to move toward brighter ones. The attractiveness and brightness are opposed to distance. The brightness of a firefly has been determined by the area of fitness function [ 90 ]. As the brightness of firefly increased, the level of goodness of solution increased. A full attraction model has been proposed that shows all fireflies will be attracted to brighter ones and similarity of all fireflies will occur if a great number of fireflies attract to a brighter one, which is measured by fitness value. So, convergence rate during the search method will occur in a slow pace.

FA has been inspired from the lightening feature of fireflies and known as swarm intelligence algorithm. FA better works in comparison with genetic algorithm (GA) and PSO in some cases. “Unit commitment,” “energy conservation,” and “complex networks” are some examples of working area of FA [ 61 ]. Fluctuation may occur when huge numbers of fireflies attract to light emission source and the searching process becomes time-consuming. To overcome these issues, neighborhood attraction FA (NaFA) is introduced, which shows that fireflies are just attracted to only some brighter points, which are outlined by previous neighbor [ 62 ].

5.10 Tabu search algorithm (TS)

Tabu search is a meta-heuristic, which was proposed by y Glover and Laguna (1997) on the basis of edge projection and making it better and it tries to make a progress in local search, which leads to a global optimized solution by taking possibility on consecutive algorithm iterations. Local heuristic search process is taken to find solution that can be deployed to combinatorial optimization paradigm [ 2 ]. The searching process in this methodology is flexible as it takes adaptive memory. The process is done during different iterations. In each iteration, a solution is found. The solution has a neighbor point that can be reached via “move.” In every move, a better solution is found, which can be stopped when no better answer is found [ 37 ]. In TS, the aspiration criteria are critical factors that lead the searching process by not considering forbidden solutions that are known by TS. In each solution, the constraints of the objective are met. So, the solutions are both feasible and time-consuming. TS process is continued by using a tabu list (TL), which is a short-term history. The short memory just keeps the recent movement, which is done by deleting the old movement when the memory is full to the maximum level [ 1 ].

The main idea of TS is to move toward solution space, which remains unexplored, which would be an opportunity to keep away from local solution. So, “tabu” movements that are recent movements are kept forbidden, which prevents from visiting previous solution points. This is proved that the method brings high-quality solutions in its iterations [ 57 ].

6. Big data analytics and Internet of Things (IOT)

Internet of things (IOT) put focus on creating an intelligent environment in which things socialize with each other by sensing, processing, communicating, and actuating activities. As IOT sensors gathered a huge amount of raw data, which is needed to be processed and analyzed, powerful tools will enforce the analytics process. This will stimulate to deploy BDA and its methods on IOT-based data. Ref. [ 51 ] proposed a four-layer model to show how BDA can help IOT-based system to work better. This model comprised of data generation, sensor communication, data processing, and data interpretation [ 51 ]. It is cited that beyond 2020 cognitive processing and optimization will be considered on IOT data processing [ 34 ]. In IOT-based systems, acquired signals from sensors are gathered and deployed for processing in frame-by-frame or batch mode. Also, gathered data in IOT system will be deployed in feature extraction, which is followed by classification stage. Machine learning algorithms will be used in data classifying [ 54 ]. Machine learning classification can be deployed on three types of data, which are supervised, semisupervised, and unsupervised [ 54 ]. In decision-making level, which is comprised of pattern recognition, deep learning methods, namely, RNN, DNN, CNN, and ANN can be used for discovering knowledge. Optimization process in IOT can be used to create an optimized cluster in IOT data [ 91 ].

In Figure 2 , the process of IOT is shown. Data is gathered from sensors. Data enters the filtering process. In this level, denoising and data cleansing happen. Also, in this level, feature extraction is considered for classification phase. After preprocessing, decision making happens on the basis of deep learning methodology ( Table 1 ). Deep learning and machine learning algorithms can be used in analyzing of data generated through IOT device, especially in the classification and decision-making phase. Both supervised and unsupervised techniques can be used in classification phase considering the data type. However, both deep learning and machine learning algorithms are eligible in deploying in decision-making phase.

IOT process.

Deep learning and machine learning techniques on IOT phases.

7. Future research directions

For feature endeavors, it is proposed to work on application of big data analytics methods on IOT fog and edge computing. It is useful to extract patterns from hidden knowledge of data gathered from sensors deploying powerful analytical tools. Fog computing is defined as a technology that is implemented in near distance to end user, which provides local processing and storage to support different devices and sensors. Health care systems gain advantage from IOT for fog computing, which supports mobility and reliability in such systems. Health care data acquisition, processing, and storage of real-time data are done in edge, cloud, and fog layer [ 47 ]. In future research, the area that machine learning algorithms can provide techniques for fog computing can be on the focus. IOT data captured from smart houses needs analytical algorithms to overcome the complexity of offline and online data gathered in processing, classification, and also next best action, or even pattern recognition [ 81 ]. Hospital information system creates “life sciences data,” “clinical data,” “administrative data,” and “social network data.” These data sources are overwhelmed with illness predictions, medical research, or even management and control of disease [ 39 ]. Big data analytics can be a future subject by helping HIS to cover data processing and disease pattern recognition.

Smart house creates ground for real-time data with high complexity, which entitles big data analytics to overcome such sophistication. Classical methods of data analyzing lost their ability in front of evolutionary methods of classification and clustering. So graphic processing unit (GPU) for machine learning and data mining purposes bring advantage for large scale dataset [ 7 ], which leads the applications into lower cost of data analytics. Another way to create future research is to work over different frameworks like Spark, which is an in-memory computation, and with the help of big data analytics, optimization problems can be solved [ 20 ].

Deployment of natural language processing (NLP) in text classification can be accompanied by different methods like CNN and RNN. These methods can gain the result with higher accuracy and lower time (Li et al., 2018).

Predictive analytics offered by big data analytics works on developing predictive models to analyze large volume data both structured and unstructured with the goal of identifying hidden patterns and relations between variables in near future [ 76 ]. Big data analytics can help cognitive computing, and behavior pattern recognition deploys deep learning technique to predict future action as it is used to predict cancer in health care system [ 59 ]. It also leads organizations to understand their problems [ 13 ].

So, future research can be focused on both the new area for application of different machine learning or deep learning algorithm for censored data gathered and also mixture of techniques that can create globally optimal solution with higher accuracy and lower cost. Researchers can put focus on existing problems of industries through mixed application of machine learning and deep learning techniques, which may results in optimize solution with lower cost and higher speed. They also can take identified algorithms in new area of industries to solve problems, create insight, and identify hidden patterns.

In summary, future research can be done as it is shown in Figure 3 .

Future research on big data analytics (BDA).

8. Conclusion

This chapter has been attempted to give an overview on big data analytics and its subfields, which are machine learning and deep learning techniques. As it is cited before, big data analytics has been generated to overcome the complexity of data managing and also create and bring knowledge into organizations to empower the performances. In this chapter, DNN, RNN, and CNN have been introduced as deep learning methods, and classification, clustering, and evolutionary techniques have been overviewed. Also, a glance at some techniques of every field has been given. Also, the application of machine learning and deep learning in IOT-based data is shown in order to make IOT data analytics much more powerful in phase of classification and decision-making. It has been identified that on the basis of rapid speed of data generation through IOT sensors, big data analytics methods have been widely used for analyzing real-time data, which can solve the problem of complexity of data processing. Hospital information systems (HIS), smart cities, and smart houses take benefits of to-the-point data processing by deploying fog and cloud platforms. The methods are not only deployed to create a clear picture of clusters and classifications of data but also to create insight for future behavior by pattern recognition. A wide variety of future research has been proposed by researchers, from customer pattern recognition to predict illness like cancer and all in between are comprised in area of big data analytics algorithms.

Acknowledgments

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

• 1. Bożejko W et al. Parallel tabu search for the cyclic job shop scheduling problem. Computers & Industrial Engineering. 2018; 113 :512-524
• 2. Kiziloz H, Dokeroglu T. A robust and cooperative parallel tabu search algorithm for the maximum vertex weight clique problem. Computers & Industrial Engineering. 2018; 118 :54-66
• 3. Acharya U et al. Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowledge-Based Systems. 2017; 132 :62-71
• 4. Babu GP, Murty M. A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm. Pattern Recognition Letters. 1993; 14 (10):763-769
• 5. Bonyadi MR, Michalewicz Z. Particle swarm optimization for single objective continuous space problems: A review. Evolutionary Computation. 2017; 25 (1):1-54
• 6. Caliskan A et al. Classification of high resolution hyperspectral remote sensing data using deep neural networks. Engineering Applications of Artificial Intelligence. 2018; 67 :14-23
• 7. Cano A. A survey on graphic processing unit computing for large-scale data mining. WIREs Data Mining and Knowledge Discovery. 2017; 8 (1):e1232. DOI: 10.1002/widm.1232
• 8. Caraveo C et al. Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation. Applied Soft Computing. 2016; 43 :131-142
• 9. Castillo O, Amador-Angulo L. A generalized type-2 fuzzy logic approach for dynamic parameter adaptation in bee colony optimization applied to fuzzy controller design. Information Sciences. 2018; 460-461 :476-496
• 10. Chen J et al. The synergistic effects of IT-enabled resources on organizational capabilities and firmperformance. Information and Management. 2012; 49 (34):140-152
• 11. Chou J et al. Metaheuristic optimization within machine learning-based classification system for early warnings related to geotechnical problems. Automation in Construction. 2016; 68 :65-80
• 12. Côrte-Real A et al. Assessing business value of Big Data Analytics in European firms. Journal of Business Research. 2017; 70 :379-390
• 13. Côrte-Real N et al. Unlocking the drivers of big data analytics value in firms. Journal of Business Research. 2019; 97 :160-173
• 14. Delice Y et al. A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing. Journal of Intelligent Manufacturing. 2017; 28 (1):23-36
• 15. Ding S et al. Extreme learning machine: Algorithm, theory and applications. Artificial Intelligent Review. 2015; 44 (1):103-115
• 16. Dong J, Yang C. Business value of big data analytics: A systems-theoretic approach and empirical test. In: Information & Management. 2018. [In Press]
• 17. Dorigo M. Ant Colony Optimization: New Optimization Techniques in Engineering. Berlin Heidelberg: Springer-Verlag; 1991. pp. 101-117
• 18. Esposito C et al. A knowledge-based platform for Big Data analytics based on publish/subscribe services and stream processing. Knowledge-Based Systems. 2015; 79 :3-17
• 19. Feng L et al. Rough extreme learning machine: A new classification method based on uncertainty measure. Neurocomputing. 2019; 325 :269-282
• 20. Gonzalez-Lopez J et al. Distributed nearest neighbor classification for large-scale multi-label data on spark. Future Generation Computer Systems. 2018; 87 :66-82
• 21. Gallicchio C et al. Deep reservoir computing: A critical experimental analysis. Neurocomputing. 2017; 268 :87-99
• 22. Gandomi A, Haider M. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management. 2015; 35 (2):137-144
• 23. German F et al. Do retailers benefit from deploying customer analytics? Journal of Retailing. 2014; 90 :587-593
• 24. Ghosh A et al. Aggregation pheromone density based data clustering. Information Sciences. 2008; 178 :2816-2831
• 25. Gonzalez-Abril L et al. Handling binary classification problems with a priority class by using support vector machines. Applied Soft Computing. 2017; 61 :661-669
• 26. Gu X, Angelov P. Self-organizing fuzzy logic classifier. Information Sciences. 2018; 447 :36-51
• 27. Guo Y et al. Deep learning for visual understanding: A review. Neurocomputing. 2016; 187 :27-48
• 28. Harfouchi F et al. Modified multiple search cooperative foraging strategy for improved artificial bee colony optimization with robustness analysis. Soft Computing. 2017; 22 (19)
• 29. Huang J et al. A clustering method based on extreme learning machine. Neurocomputing. 2018; 227 :108-119
• 30. Hüllermeier E. Does machine learning need fuzzy logic? Fuzzy Sets and Systems. 2015; 281 :292-299
• 31. Jan B et al. Deep learning in big data analytics: A comparative study. Computers and Electrical Engineering. 2017; 75 :1-13
• 32. Jiang P, Chen J. Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation. Neurocomputing. 2016; 198 :40-47
• 33. Jiang S et al. Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department. Expert Systems With Applications. 2017; 82 :216-230
• 34. Ko Y. How to use negative class information for Naive Bayes classification. Information Processing and Management. 2017; 53 (6):1255-1268
• 35. Koonce D, Tsaib S. Using data mining to find patterns in genetic algorithm solutions to a job shop schedule. Computers & Industrial Engineering. 2000; 38 (3):361-374
• 36. Kozak J, Boryczka U. Collective data mining in the ant colony decision tree approach. Information Sciences. 2016; 372 :126-147
• 37. Kwon O et al. Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management. 2014; 34 (3):387-394
• 38. Lee I, Lee K. The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons. 2015; 58 (4):1-10
• 39. Li J et al. Medical big data analysis in hospital information system. In: Big Data on Real-World Applications. 2016. Chapter 4
• 40. Loebbecke C, Picot A. Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. Journal of Strategic Information Systems. 2015; 24 (3):149-157
• 41. Lohrmann C, Luukka P. A novel similarity classifier with multiple ideal vectors based on k-means clustering. Decision Support Systems. 2018; 111 :27-37
• 42. Martí R et al. Tabu search for the dynamic bipartite drawing problem. Computers and Operations Research. 2018; 91 :1-12
• 43. Maulik U et al. Genetic algorithm-based clustering technique. Pattern Recognition. 2000; 33 (9):1455-1465
• 44. Mavrovounioti M, Yang S. Training neural networks with ant colony optimization algorithms for pattern classification. Journal of Soft Computing. 2015; 19 (6):1511-1522
• 45. Miao Z et al. Robust tracking control of uncertain dynamic nonholonomic systems using recurrent neural networks. Neurocomputing. 2014; 142 :216-227
• 46. Mohan B, Baskaran R. A survey: Ant colony optimization based recent research and implementation on several engineering domain. Expert Systems with Applications. 2012; 39 (4):4618-4627
• 47. Mutlag AA et al. Enabling technologies for fog computing in health care IoT systems. Future Generation Computer Systems. 2019; 90 :62-78
• 48. Najafabadi M et al. Deep learning applications and challenges in big data analytics. Journal of Big Data. 2015:1-21. DOI: 10.1186/s40537-014-0007-7
• 49. Nguyen T et al. Big data analytics in supply chain management: A state-of-the-art literature review. Computers and Operations Research. 2018; 98 :254-264
• 50. Ning J et al. A best-path-updating information-guided ant colony optimization algorithm. Information Sciences. 2018; 433-434 :142-162
• 51. Osipov V, Osipova M. Space–time signal binding in recurrent neural networks with controlled elements. Neurocomputing. 2018; 308 :194-204
• 52. Panda M, Abraham A. Hybrid evolutionary algorithms for classification data mining. In: Neural Computing & Applications. 2014; 26 (3):507-523
• 53. Peng H et al. An unsupervised learning algorithm for membrane computing. Information Sciences. 2015; 304 :80-91
• 54. Peng Y et al. Orthogonal extreme learning machine for image classification. Neurocomputing. 2017; 266 :458-464
• 55. Qawaqneh Z et al. Age and gender classification from speech and face images by jointly fine-tuned deep neural networks. Expert Systems With Applications. 2017; 85 :78-86
• 56. Ramsingh J, Bhuvaneswari V. An efficient map reduce-based hybrid NBC-TFIDF algorithm to mine the public sentiment on diabetes mellitus—A big data approach. Journal of King Saud University – Computer and Information Sciences. 2018. [In Press]
• 57. Rathore M et al. Urban planning and building smart cities based on the Internet of Things using Big Data analytics. Computer Networks. 2016; 101 :63-80
• 58. Ruan X, Zhang Y. Blind sequence estimation of MPSK signals using dynamically driven recurrent neural networks. Neurocomputing. 2014; 129 :421-427
• 59. Sekaran K et al. Deep learning convolutional neural network (CNN) with Gaussian mixture model for predicting pancreatic cancer. Multimedia Tools and Applications. 2019:1-15. DOI: 10.1007/s11042-019-7419-5
• 60. Shah S, Kusiak A. Data mining and genetic algorithm based gene/SNP selection. Artificial Intelligence in Medicine. 2004; 31 (3):183-196
• 61. Shanthamallu U et al. A brief survey of machine learning methods and their sensor and IoT applications. In: 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA). 2017. DOI: 10.1109/IISA.2017.8316459
• 62. Shunmugapriya P, Kanmani S. A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid). Swarm and Evolutionary Computation. 2017; 36 :27-36
• 63. Sikora R, Piramuthu S. Framework for efficient feature selection in genetic algorithm based data mining. European Journal of Operational Research. 2007; 180 (2):723-737
• 64. Silva M, Cunha C. A tabu search heuristic for the uncapacitated single allocation p-hub maximal covering problem. European Journal of Operational Research. 2017; 262 (3):954-965
• 65. Srinivasa KG et al. A self-adaptive migration model genetic algorithm for data mining applications. Information Sciences. 2007; 177 (20):4295-4313
• 66. Taherkhani M, Safabakhsh R. A novel stability-based adaptive inertia weight for particle swarm optimization. Applied Soft Computing. 2016; 38 :281-295
• 67. Wan Y et al. Twin extreme learning machines for pattern classification. Neurocomputing. 2017; 260 :235-244
• 68. Wang H et al. Firefly algorithm with neighborhood attraction. Information Sciences. 2017; 382-383 :374-387
• 69. Wang H et al. Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism. Journal of Soft Computing. 2017; 21 (18):5325-5339
• 70. Wang Q et al. Local kernel alignment based multi-view clustering using extreme learning machine. Neurocomputing. 2018; 275 :1099-1111
• 71. Wu J et al. A patent quality analysis and classification system using self-organizing maps with support vector machine. Applied Soft Computing. 2016; 41 :305-316
• 72. Zhang L, Zhang Q. A novel ant-based clustering algorithm using the kernel method. Information Sciences. 2011; 181 :4658-4672
• 73. Zhang X et al. An overview of recent developments in Lyapunov–Krasovskii functionals and stability criteria for recurrent neural networks with time-varying delays. Neurocomputing. 2018; 313 :392-401
• 74. Zhu S, Shen Y. Robustness analysis for connection weight matrix of global exponential stability recurrent neural networks. Neurocomputing. 2013; 101 :370-374
• 75. Wang Y et al. Integrated big data analytics-enabled transformation model: Application to health care. Information and Management. 2018; 55 (1):64-79
• 76. Wang Y, Hajli N. Exploring the path to big data analytics success in healthcare. Journal of Business Research. 2017; 70 :287-299
• 77. Iqbal R et al. Big data analytics: Computational intelligence techniques and application areas. Technological Forecasting & Social Change. 2018. [In Press]
• 78. Wamba S et al. Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research. 2017; 70 :356-365
• 79. Zhang Q et al. A survey on deep learning for big data. Information Fusion. 2018; 42 :146-157
• 80. Liu W et al. A survey of deep neural network architectures and their applications. Neurocomputing. 2017; 234 :11-26
• 81. Yassine A et al. IoT big data analytics for smart homes with fog and cloud computing. Future Generation Computer Systems. 2019; 91 :563-573
• 82. Yin Z et al. A-optimal convolutional neural network. Neural Computings & Applications. 2016; 30 (7):2295-2304
• 83. Wang S et al. Convolutional neural network-based hidden Markov models for rolling element bearing fault identification. Knowledge-Based Systems. 2018; 144 :65-76
• 84. Shi X et al. Tracking topology structure adaptively with deep neural networks. Neural Computing & Application. 2017; 30 (11):3317-3326
• 85. Zhou L et al. Machine learning on big data: Opportunities and challenges. Neurocomputing Journal. 2017; 237 :350-361
• 86. Tack C. Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy. Musculoskeletal Science and Practice. 2018; 39 :164-169
• 87. Tang L et al. A novel perspective on multiclass classification: Regular simplex support vector machine. Information Sciences. 2018; 480 :324-338
• 88. Xia M et al. A hybrid method based on extreme learning machine and k-nearest neighbor for cloud classification of ground-based visible cloud image. Neurocomputing. 2015; 160 :238-249
• 89. Onan A. A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer. Expert Systems with Applications. 2015; 42 (20):6844-6852
• 90. Gou J et al. A generalized mean distance-based k-nearest neighbor classifier. Expert Systems With Applications. 2019; 115 :356-372
• 91. Pan Z et al. A new k-harmonic nearest neighbor classifier based on the multi-local means. Expert Systems With Applications. 2017; 67 :115-125
• 92. Xu S, Wang J. Dynamic extreme learning machine for data stream classification. Neurocomputing. 2017; 238 :433-449
• 93. Du G et al. Study on density peaks clustering based on k-nearest neighbors and principal component analysis. Knowledge-Based Systems. 2016; 99 :135-145
• 94. Yu S et al. Two improved k-means algorithms. Applied Soft Computing. 2018; 68 :747-755
• 95. Tabakhi S et al. Gene selection for microarray data classification using a novel ant colony optimization. Neurocomputing. 2015; 168 :1024-1036
• 96. Liu H et al. A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm. Applied Soft Computing. 2018; 68 :360-376
• 97. Hong T et al. A multi-level ant-colony mining algorithm for membership functions. Information Sciences. 2012; 182 (1):3-14
• 98. Kuo RJ et al. Integration of growing self-organizing map and bee colony optimization algorithm for part clustering. Computers & Industrial Engineering. 2018; 120 :251-265
• 99. Verma O et al. Opposition and dimensional based modified firefly algorithm. Expert Systems With Applications. 2016; 44 :168-176
• 100. Janakiraman S. A hybrid ant colony and artificial bee colony optimization algorithm-based cluster head selection for IoT. Procedia Computer Science. 2018; 143 :360-366
• 101. Tsai C et al. Metaheuristic algorithms for healthcare: Open issues and challenges. Computers and Electrical Engineering. 2016; 53 :421-434
• 102. Villarrubia G et al. Artificial neural networks used in optimization problems. Neurocomputing. 2018; 272 :10-16
• 103. Wari E, Zhu W. A survey on metaheuristics for optimization in food manufacturing. Applied Soft Computing. 2016; 46 :328-343
• 104. Wu J et al. Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing. 2015; 148 :136-142
• 105. Yang F et al. A new approach to non-fragile state estimation for continuous neural networks with time-delays. Neurocomputing. 2016; 197 :205-211

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Continue reading from the same book

Published: 19 February 2020

By Nityashree Nadar and R. Kamatchi

919 downloads

By Alberto Cano

993 downloads

By Leila Kerkeni, Youssef Serrestou, Mohamed Mbarki, ...

6439 downloads

IntechOpen Author/Editor? To get your discount, log in .

Discounts available on purchase of multiple copies. View rates

Local taxes (VAT) are calculated in later steps, if applicable.

Support: [email protected]

Methodology

• Open access
• Published: 28 April 2022

An intelligent literature review: adopting inductive approach to define machine learning applications in the clinical domain

• Renu Sabharwal   ORCID: orcid.org/0000-0001-9728-8001 1 &
• Shah J. Miah 1  

Journal of Big Data volume  9 , Article number:  53 ( 2022 ) Cite this article

7551 Accesses

10 Citations

Metrics details

Big data analytics utilizes different techniques to transform large volumes of big datasets. The analytics techniques utilize various computational methods such as Machine Learning (ML) for converting raw data into valuable insights. The ML assists individuals in performing work activities intelligently, which empowers decision-makers. Since academics and industry practitioners have growing interests in ML, various existing review studies have explored different applications of ML for enhancing knowledge about specific problem domains. However, in most of the cases existing studies suffer from the limitations of employing a holistic, automated approach. While several researchers developed various techniques to automate the systematic literature review process, they also seemed to lack transparency and guidance for future researchers. This research aims to promote the utilization of intelligent literature reviews for researchers by introducing a step-by-step automated framework. We offer an intelligent literature review to obtain in-depth analytical insight of ML applications in the clinical domain to (a) develop the intelligent literature framework using traditional literature and Latent Dirichlet Allocation (LDA) topic modeling, (b) analyze research documents using traditional systematic literature review revealing ML applications, and (c) identify topics from documents using LDA topic modeling. We used a PRISMA framework for the review to harness samples sourced from four major databases (e.g., IEEE, PubMed, Scopus, and Google Scholar) published between 2016 and 2021 (September). The framework comprises two stages—(a) traditional systematic literature review consisting of three stages (planning, conducting, and reporting) and (b) LDA topic modeling that consists of three steps (pre-processing, topic modeling, and post-processing). The intelligent literature review framework transparently and reliably reviewed 305 sample documents.

Introduction

Organizations are continuously harnessing the power of various big data adopting different ML techniques. Captured insights from big data may create a greater impact to reshape their business operations and processes. As a vital technique, big data analytics methods are used to transform complicated and huge amounts of data, known as ‘Big Data, in order to uncover hidden patterns, new learning, untold facts or associations, anomalies, and other perceptions [ 41 ]. Big Data alludes to the enormous amount of data that a traditional database management system cannot handle. In most of the cases, traditional software functions would be inadequate to analyze or process them. Big data are characterized by the 5 V’s, which refers to volume, variety, velocity, veracity, and value [ 22 ]. ML is a vital approach to design useful big data analytics techniques, which is a rapidly growing sub-field in information sciences that deals with all these characteristics. ML employs numerous methods for machines to learn from past experiences (e.g., past datasets) reducing the extra burden of writing codes in traditional programming [ 7 , 26 ]. Clinical care enterprises face a huge challenge due to the increasing demand of big data processing to improve clinical care outcomes. For example, an electronic health record contains a huge amount of patient information, drug administration, imaging data using various modalities. The variety and quantity of the huge data provide in the clinical domain as an ideal topic to appraise the value of ML in research.

Existing ML approaches, such as Oala et al. [ 35 ] proposed an algorithmic framework that give a path towards the effective and reliable application of ML in the healthcare domain. In conjunction with their systematic review, our research offers a smart literature review that consolidates a traditional literature review followed the PRISMA framework guidelines and topic modeling using LDA, focusing on the clinical domain. Most of the existing literature focused on the healthcare domain [ 14 , 42 , 49 ] are more inclusive and of a broader scope with a requisite of medical activities, whereas our research is primarily focused is clinical, which assist in diagnosing and treating patients as well as includes clinical aspects of medicine.

Since clinical research has developed, the area has become increasingly attractive to clinical researchers, in particular for learning insights of ML applications in clinical practices . This is because of its practical pertinence to clinical patients, professionals, clinical application designers, and other specialists supported by the omnipresence of clinical disease management techniques. Although the advantage is presumed for the target audience, such as self-management abilities (self-efficacy and investment behavior) and physical or mental condition of life amid long-term ill patients, clinical care specialists (such as further developing independent direction and providing care support to patients), their clinical care have not been previously assessed and conceptualized as a well-defined and essential sub-field of health care research. It is important to portray similar studies utilizing different types of review approaches in the aspect of the utilization of ML/DL and its value. Table 1 represents some examples of existing studies with various points and review approaches in the domain.

Although the existing studies included in Table 1 give an understanding of designated aspects of ML/DL utilization in clinical care, they show a lack of focus on how key points addressed in existing ML/DL research are developing. Further to this, they indicate a clear need towards an understanding of multidisciplinary affiliations and profiles of ML/DL that could provide significant knowledge to new specialists or professionals in this space. For instance, Brnabic and Hess [ 8 ] recommended a direction for future research by stating that “ Future work should routinely employ ensemble methods incorporating various applications of machine learning algorithms” (p. 1).

ML tools have become the central focus of modern biomedical research, because of better admittance to large datasets, exponential processing power, and key algorithmic developments allowing ML models to handle increasingly challenging data [ 19 ]. Different ML approaches can analyze a huge amount of data, including difficult and abnormal patterns. Most studies have focused on ML and its impacts on clinical practices [ 2 , 9 , 10 , 24 , 26 , 34 , 43 ]. Fewer studies have examined the utilization of ML algorithms [ 11 , 20 , 45 , 48 ] for more holistic benefits for clinical researchers.

ML becomes an interdisciplinary science that integrates computer science, mathematics, and statistics. It is also a methodology that builds smart machines for artificial intelligence. Its applications comprise algorithms, an assortment of instructions to perform specific tasks, crafted to independently learn from data without human intercession. Over time, ML algorithms improve their prediction accuracy without a need for programming. Based on this, we offer an intelligent literature review using traditional literature review and Latent Dirichlet Allocation (LDA Footnote 1 ) topic modeling in order to meet knowledge demands in the clinical domain. Theoretical measures direct the current study results because previous literature provides a strong foundation for future IS researchers to investigate ML in the clinical sector. The main aim of this study is to develop an intelligent literature framework using traditional literature. For this purpose, we employed four digital databases -IEEE, Google Scholar, PubMed, and Scopus then performed LDA topic modeling, which may assist healthcare or clinical researchers in analyzing many documents intelligently with little effort and a small amount of time.

Traditional systematic literature is destined to be obsolete, time-consuming with restricted processing power, resulting in fewer sample documents investigated. Academic and practitioner-researchers are frequently required to discover, organize, and comprehend new and unexplored research areas. As a part of a traditional literature review that involves an enormous number of papers, the choice for a researcher is either to restrict the number of documents to review a priori or analyze the study using some other methods.

The proposed intelligent literature review approach consists of Part A and Part B, a combination of traditional systematic literature review and topic modeling that may assist future researchers in using appropriate technology, producing accurate results, and saving time. We present the framework below in Fig.  1 .

Proposed intelligent literature review framework

The traditional literature review identified 534,327 articles embraces Scopus (24,498), IEEE (2558), PubMed (11,271), and Google Scholar (496,000) articles, which went through three stages–Planning the review, conducting the review, and reporting the review and analyzed 305 articles, where we performed topic modeling using LDA.

We follow traditional systematic literature review methodologies [ 25 , 39 , 40 ] including a PRISMA framework [ 37 ]. We review four digital databases and deliberately develop three stages entailing planning, conducting, and reporting the review (Fig.  2 ).

Traditional literature review three stages

Planning the review

Research articles : the research articles are classified using some keywords mentioned below in Tables 2 , 3 .

Digital database : Four databases (IEEE, PubMed, Scopus, and Google Scholar) were used to collect details for reviewing research articles.

Review protocol development : We first used Scopus to search the information and found many studies regarding this review. We then searched PubMed, IEEE, and Google scholar for articles and extracted only relevant papers matching our keywords and review context based on their full-text availability.

Review protocol evaluation : To support the selection of research articles and inclusion and exclusion criteria, the quality of articles was explored and assessed to appraise their suitability and impartiality [ 44 ]. Only articles with keywords “machine learning” and “clinical” in document titles and abstracts were selected.

Conducting the review

The second step is conducting the review, which includes a description of Search Syntax and data synthesis.

Search syntax Table 4 details the syntax used to select research articles.

Data synthesis

We used a qualitative meta-synthesis technique to understand the methodology, algorithms, applications, qualities, results, and current research impediments. Qualitative meta-synthesis is a coherent approach for analyzing data across qualitative studies [ 4 ]. Our first search identified 534,327 papers, comprising Scopus (24,498), IEEE (2,558), PubMed (11,271), and Google Scholar (496,000) articles with the selected keywords. After subjecting this dataset to our inclusion and exclusion criteria, articles were reduced to Scopus (181), IEEE (62), PubMed (37), and Google Scholar (46) (Fig.  3 ).

PRISMA framework of traditional literature review

Reporting the review

This section displays the result of the traditional literature review.

Demonstration of findings

A search including linear literature and citation chaining was acted in digital databases, and the resulted papers were thoroughly analyzed to choose only the most pertinent articles, at last, 305 articles were included for the Part B review. Information of such articles were classified, organized, and demonstrated to show the finding.

Report the findings

The word cloud is displayed on the selected 305 research articles which give an overview of the frequency of the word within those 305 research articles. The chosen articles are moved to the next step to perform the conversion of PDF files to text documents for performing LDA topic modeling (Fig. 4 ).

Word cloud on 305 articles

Conversion of pdf files to a text document

The Python coding is used to convert pdf files shared on GitHub https://github.com/MachineLearning-UON/Topic-modeling-using-LDA.git . The one text document is prepared with 305 research papers collected from a traditional literature review.

Topic modelling for intelligent literature review

Our intelligent literature review is developed using a combination of traditional literature review and topic modeling [ 22 ]. We use topic modeling—probability generating, a text-mining technique widely used in computer science for text mining and data recovery. Topic modeling is used in numerous papers to analyze [ 1 , 5 , 17 , 36 ] and use various ML algorithms [ 38 ] such as Latent Semantic Indexing (LSI), Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Parallel Latent Dirichlet Allocation (PLDA), and Pachinko Allocation Model (PAM). We developed the LDA-based methodological framework so it would be most widely and easily used [ 13 , 17 , 21 ] as a very elementary [ 6 ] approach. LDA is an unsupervised and probabilistic ML algorithm that discovers topics by calculating patterns of word co-occurrence across many documents or corpus [ 16 ]. Each LDA topic is distributed across each document as a probability.

While there are numerous ways of conducting a systematic literature review, most strategies require a high expense of time and prior knowledge of the area in advance. This study examined the expense of various text categorization strategies, where the assumptions and cost of the strategy are analyzed [ 5 ]. Interestingly, except manually reading the articles and topic modeling, all the strategies require prior knowledge of the articles' categories and high pre-examination costs. However, topic modeling can be automated, alternate the utilization of researchers' time, demonstrating a perfect match for the utilization of topic modeling as a part of an Intelligent literature review. Topic modeling has been used in a few papers to categorize research papers presented in Table 5 .

The articles/papers in the above table analyzed are speeches, web documents, web posts, press releases, and newspapers. However, none of those have developed the framework to perform traditional literature reviews from digital databases then use topic modeling to save time. However, this research points out the utilization of LDA in academics and explores four parameters—text pre-processing, model parameters selection, reliability, and validity [ 5 ]. Topic modeling identifies patterns of the repetitive word across a corpus of documents. Patterns of word co-occurrence are conceived as hidden ‘topics’ available in the corpus. First, documents must be modified to be machine-readable, with only their most informative features used for topic modeling. We modify documents in a three-stage process entailing pre-processing, topic modeling, and post-processing, as defined in Fig.  1 earlier.

The utilization of topic modeling presents an opportunity for researchers to use advanced technology for the literature review process. Topic modeling has been utilized online and requires many statistical skills, which not all researchers have. Therefore, we have shared the codes in GitHub with the default parameter for future researchers.

Pre-processing

Székely and Brocke [ 46 ] explained that pre-processing is a seven-step process which explored below and mentioned in Fig.  1 as part B:

Load data—The text data file is imported using the python command.

Optical character recognition—using word cloud, characters are recognized.

Filtering non-English words—non-English words are removed.

Document tokenization—Split the text into sentences and the sentences into words. Lowercase the words and remove punctuation.

Text cleaning—the text has been cleaned using portstemmer.

Word lemmatization—words in the third person are changed to the first person, and past and future verb tenses are changed into the present.

Stop word removal—All stop words are removed.

Topic modelling using LDA

Several research articles have been selected to run LDA topic modeling, explained in Table 5 . LDA model results present the coherence score for all the selected topics and a list of the most frequently used words for each.

Post-processing

The goal of the post-processing stage is to identify and label topics and topics relevant for use in the literature review. The result of the LDA model is presented as a list of topics and probabilities of each document (paper). The list is utilized to assign a paper to a topic by arranging the list by the highest probability for each paper for each topic. All the topics contain documents that are like each other. To reduce the risk of error in topic identification, a combination of inspecting the most frequent words for each topic and a paper view is used. After the topic review, it will present in the literature review.

Following the intelligent literature review, results of the LDA model should be approved or validated by statistical, semantic, or predictive means. Statistical validation defines the mutual information tests of result fit to model assumptions; semantics validation requires hand-coding to decide if the importance of specific words varies significantly and as expected with tasks to different topics which is used in the current study to validate LDA model result; and predictive validation refers to checking if events that ought to have expanded the prevalence of particular topic if out interpretations are right, did so [ 6 , 21 ].

LDA defines that each word in each document comes from a topic, and the topic is selected from a set of keywords. So we have two matrices:

ϴtd = P(t|d) which is the probability distribution of topics in documents

Фwt = P(w|t), which is the probability distribution of words in topics

And, we can say that the probability of a word given document, i.e., P(w|d), is equal to:

where T is the total number of topics; likewise, let’s assume there are W keywords for all the documents.

If we assume conditional independence, we can say that

And hence P(w|d) is equal to

that is the dot product of ϴtd and Фwt for each topic t.

Our systematic literature review identified 305 research papers after performing a traditional literature review. After executing LDA topic modeling, only 115 articles show the relevancy with our topic "machine learning application in clinical domain'. The following stages present LDA topic modeling process.

The 305 research papers were stacked into a Python environment then converted into a single text file. The seven steps have been carried out, described earlier in Pre-processing .

• Topic modeling

The two main parameters of the LDA topic model are the dictionary (id2word)-dictionary and the corpus—doc_term_matrix. The LDA model is created by running the command:

# Creating the object for LDA model using gensim library

LDA = gensim.models.ldamodel.LdaModel

# Build LDA model

lda_model = LDA(corpus=doc_term_matrix, id2word = dictionary, num_topics=20, random_state=100,

chunksize = 1000, passes=50,iterations=100)

In this model, ‘num_topics’ = 20, ‘chunksize’ is the number of documents used in each training chunk, and ‘passes’ is the total number of training passes.

Firstly, the LDA model is built with 20 topics; each topic is represented by a combination of 20 keywords, with each keyword contributing a certain weight to a topic. Topics are viewed and interpreted in the LDA model, such as Topic 0, represented as below:

(0, '0.005*"analysis" + 0.005*"study" + 0.005*"models" + 0.004*"prediction" + 0.003*"disease" + 0.003*"performance" + 0.003*"different" + 0.003*"results" + 0.003*"patient" + 0.002*"feature" + 0.002*"system" + 0.002*"accuracy" + 0.002*"diagnosis" + 0.002*"classification" + 0.002*"studies" + 0.002*"medicine" + 0.002*"value" + 0.002*"approach" + 0.002*"variables" + 0.002*"review"'),

Our approach to finding the ideal number of topics is to construct LDA models with different numbers of topics as K and select the model with the highest coherence value. Selecting the ‘K' value that denotes the end of the rapid growth of topic coherence ordinarily offers significant and interpretable topics. Picking a considerably higher value can provide more granular sub-topics if the ‘K’ selection is too large, which can cause the repetition of keywords in multiple topics.

Model perplexity and topic coherence values are − 8.855378536321144 and 0.3724024189689453, respectively. To measure the efficiency of the LDA model is lower the perplexity, the better the model is. Topics and associated keywords were then examined in an interactive chart using the pyLDAvis package, which presents the topics are 20 and most salient terms in those 20 topics, but these 20 topics overlap each other as shown in Fig.  5 , which means the keywords are repeated in these 20 topics and topics are overlapped, which means so decided to use num_topics = 9 and presented PyLDAvis Figure below. Each bubble on the left-hand side plot represents a topic. The bigger the bubble is, the more predominant that topic is. A decent topic will have a genuinely big, non-overlapping bubble dispersed throughout the graph instead of grouped in one quadrant. A topic model with many topics will typically have many overlaps, small-sized bubbles clustered in one locale of the graph, as shown in Fig.  6 .

PyLDAvis graph with 20 topics in the clinical domain

PyLDAvis graph with nine vital topics in the clinical domain

Each bubble addresses a generated topic. The larger the bubble, the higher percentage of the number of keywords in the corpus is about that topic which can be seen on the GitHub file. Blue bars address the general occurrence of each word in the corpus. If no topic is selected, the blue bars of the most frequently used words are displayed, as depicted in Fig.  6 .

The further the bubbles are away from each other, the more various they are. For example, we can tell that topic 1 is about patient information and studies utilized deep learning to analyze the disease, which can be seen in GitHub file codes ( https://github.com/MachineLearning-UON/Topic-modeling-using-LDA.git ) and presented in Fig.  7 .

PyLDAvis graph with topic 1

Red bars give the assessed number of times a given topic produced a given term. As you can see from Fig.  7 , there are around 4000 of the word 'analysis', and this term is utilized 1000 times inside topic 1. The word with the longest red bar is the most used by the keywords having a place with that topic.

A good topic model will have big and non-overlapping bubbles dispersed throughout the chart. As we can see from Fig.  6 , the bubbles are clustered within one place. One of the practical applications of topic modeling is discovering the topic in a provided document. We find out the topic number with the highest percentage contribution in that document, as shown in Fig.  8 .

Dominant topics with topic percentage contribution

The next stage is to process the discoveries and find a satisfactory depiction of the topics. A combination of evaluating the most continuous words utilized to distinguish the topic. For example, the most frequent words for the papers in topic 2 are "study" and "analysis", which indicate frequent words for ML usage in the clinical domain.

The topic name is displayed with the topic number from 0 to 8, which represents in the Table 6 , which includes the Topic number and Topic words.

The result represents the percentage of the topics in all documents, which presents that topic 0 and topic 6 have the highest percentage and used in 58 and 57 documents, respectively, with 115 papers. The result of this research was an overview of the exploration areas inside the paper corpus, addressed by 9 topics.

This paper presented a new methodology that is uncommon in scholarly publications. The methodology utilizes ML to investigate sample articles/papers to distinguish research directions. Even though the structure of the ML-based methodology has its restrictions, the outcomes and its ease of use leave a promising future for topic modeling-based systematic literature reviews.

The principal benefit of the methodological framework is that it gives information about an enormous number of papers, with little effort on the researcher's part, before time-exorbitant manual work is to be finished. By utilizing the framework, it is conceivable to rapidly explore a wide range of paper corpora and assess where the researcher's time and concentration should be spent. This is particularly significant for a junior researcher with minimal earlier information on a research field. If default boundaries and cleaning settings can be found for the steps in the framework, a completely programmed gathering of papers could be empowered, where limited works have been introduced to accomplish an overview of research directions.

From a literature review viewpoint, the advantage of utilizing the proposed framework is that the inclusion and exclusion selection of papers for a literature review will be delayed to a later stage where more information is given, resulting in a more educated dynamic interaction. The framework empowers reproducibility, as every step can be reproduced in the systematic review process that ultimately empowers with transparency. The whole process has been demonstrated as a case concept on GitHub by future researchers.

The study has introduced an intelligent literature review framework that uses ML to analyze existing research documents or articles. We demonstrate how topic modeling can assist literature review by reducing the manual screening of huge quantities of literature for more efficient use of researcher time. An LDA algorithm provides default parameters and data cleaning steps, reducing the effort required to review literature. An additional advantage of our framework is that the intelligent literature review offers accurate results with little time, and it comprises traditional ways to analyze literature and LDA topic modeling.

This framework is constructed in a step-by-step manner. Researchers can use it efficiently because it requires less technical knowledge than other ML algorithms. There is no restriction on the quantity of the research papers it can measure. This research extends knowledge to similar studies in this field [ 12 , 22 , 23 , 26 , 30 , 46 ] which present topic modeling. The study acknowledges the inspiring concept of smart literature defined by Asmussen and Møller [ 3 ]. The researchers previously provided a brief description of how LDA is utilized in topic modeling. Our research followed the basic idea but enhanced its significance to broaden its scale and focus on a specific domain such as the clinical domain to produce insights from existing research articles. For instance, Székely and Vom [ 46 ] utilized natural language processing to analyze 9514 sustainability reports published between 1999 and 2015. They identified 42 topics but did not develop any framework for future researchers. This was considered a significant gap in the research. Similarly, Kushwaha et al. [ 22 ] used a network analysis approach to analyze 10-year papers without providing any clear transparent outcome (e.g., how the research step-by-step produces an outcome). Likewise, Asmussen and Møller [ 3 ] developed a smart literature review framework that was limited to analyzing 650 sample articles through a single method. However, in our research, we developed an intelligent literature review that combines traditional and LDA topic modeling, so that future researchers can get assistance to gain effective knowledge regarding literature review when it becomes a state-of-the-art in research domains.

Our research developed a more effective intelligent framework, which combines traditional literature review and topic modeling using LDA, which provides more accurate and transparent results. The results are shared via public access on GitHub using this link https://github.com/MachineLearning-UON/Topic-modeling-using-LDA.git .

This paper focused on creating a methodological framework to empower researchers, diminishing the requirement for manually scanning documents and assigning the possibility to examine practically limitless. It would assist in capturing insights of an enormous number of papers quicker, more transparently, with more reliability. The proposed framework utilizes the LDA's topic model, which gathers related documents into topics.

A framework employed topic modeling for rapidly and reliably investigating a limitless number of papers, reducing their need to read individually, is developed. Topic modeling using the LDA algorithm can assist future researchers as they often need an outline of various research fields with minimal pre-existing knowledge. The proposed framework can empower researchers to review more papers in less time with more accuracy. Our intelligent literature review framework includes a holistic literature review process (conducting, planning, and reporting the review) and an LDA topic modeling (pre-processing, topic modeling, and post-processing stages), which conclude the results of 115 research articles are relevant to the search.

The automation of topic modeling with default parameters could also be explored to benefit non-technical researchers to explore topics or related keywords in any problem domain. For future directions, the principal points should be addressed. Future researchers in other research fields should apply the proposed framework to acquire information about the practical usage and gain ideas for additional advancement of the framework. Furthermore, research in how to consequently specify model parameters could extraordinarily enhance the ease of use for the utilization of topic modeling for non-specialized researchers, as the determination of model parameters enormously affects the outcome of the framework.

Future research may be utilized more ML analytics tools as complete solution artifacts to analyze different forms of big data. This could be adopting design science research methodologies for benefiting design researchers who are interested in building ML-based artifacts [ 15 , 28 , 29 , 31 , 32 , 33 ].

Availability of data and materials

Data will be supplied upon request.

LDA is a probabilistic method for topic modeling in text analysis, providing both a predictive and latent topic representation.

Abbreviations

The Institute of Electrical and Electronics Engineers

• Machine learning
• Latent Dirichlet Allocation

Organizational Capacity

Latent Semantic Indexing

Latent Semantic Analysis

Non-Negative Matrix Factorization

Parallel Latent Dirichlet Allocation

Pachinko Allocation Model

Abuhay TM, Kovalchuk SV, Bochenina K, Mbogo G-K, Visheratin AA, Kampis G, et al. Analysis of publication activity of computational science society in 2001–2017 using topic modelling and graph theory. J Comput Sci. 2018;26:193–204.

Article   Google Scholar  

Adlung L, Cohen Y, Mor U, Elinav E. Machine learning in clinical decision making. Med. 2021;2(6):642–65.

Asmussen CB, Møller C. Smart literature review: a practical topic modeling approach to exploratory literature review. J Big Data. 2019;6(1):1–18.

Beck CT. A meta-synthesis of qualitative research. MCN Am J Mater Child Nurs. 2002;27(4):214–21.

Behera RK, Bala PK, Dhir A. The emerging role of cognitive computing in healthcare: a systematic literature review. Int J Med Informatics. 2019;129:154–66.

Blei DM. Probabilistic topic models. Commun ACM. 2012;55(4):77–84.

Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003;3:993–1022.

MATH   Google Scholar  

Brnabic A, Hess LM. Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making. BMC Med Inform Decis Mak. 2021;21(1):1–19.

Cabitza F, Locoro A, Banfi G. Machine learning in orthopedics: a literature review. Front Bioeng Biotechnol. 2018;6:75.

Chang C-H, Lin C-H, Lane H-Y. Machine learning and novel biomarkers for the diagnosis of Alzheimer’s disease. Int J Mol Sci. 2021;22(5):2761.

Connor KL, O’Sullivan ED, Marson LP, Wigmore SJ, Harrison EM. The future role of machine learning in clinical transplantation. Transplantation. 2021;105(4):723–35.

Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019;11(1):1–12.

DiMaggio P, Nag M, Blei D. Exploiting affinities between topic modeling and the sociological perspective on culture: application to newspaper coverage of US government arts funding. Poetics. 2013;41(6):570–606.

Forest P-G, Martin D. Fit for Purpose: Findings and recommendations of the external review of the Pan-Canadian Health Organizations: Summary Report: Health Canada Ottawa, ON; 2018.

Genemo H, Miah SJ, McAndrew A. A design science research methodology for developing a computer-aided assessment approach using method marking concept. Educ Inf Technol. 2016;21(6):1769–84.

Greene D, Cross JP. Exploring the political agenda of the european parliament using a dynamic topic modeling approach. Polit Anal. 2017;25(1):77–94.

Grimmer J. A Bayesian hierarchical topic model for political texts: measuring expressed agendas in Senate press releases. Polit Anal. 2010;18(1):1–35.

Grimmer J, Stewart BM. Text as data: the promise and pitfalls of automatic content analysis methods for political texts. Polit Anal. 2013;21(3):267–97.

Hassan N, Slight R, Weiand D, Vellinga A, Morgan G, Aboushareb F, et al. Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review. Int J Med Inform. 2021;150:104457.

Hirt R, Koehl NJ, Satzger G, editors. An end-to-end process model for supervised machine learning classification: from problem to deployment in information systems. Designing the Digital Transformation: DESRIST 2017 Research in Progress Proceedings of the 12th International Conference on Design Science Research in Information Systems and Technology Karlsruhe, Germany 30 May-1 Jun; 2017: Karlsruher Institut für Technologie (KIT).

Koltsova O, Koltcov S. Mapping the public agenda with topic modeling: the case of the Russian live journal. Policy Internet. 2013;5(2):207–27.

Kushwaha AK, Kar AK, Dwivedi YK. Applications of big data in emerging management disciplines: a literature review using text mining. Int J Inf Manag Data Insights. 2021;1(2):100017.

Google Scholar  

Li S, Wang H. Traditional literature review and research synthesis. The Palgrave handbook of applied linguistics research methodology. 2018:123–44.

Magrabi F, Ammenwerth E, McNair JB, De Keizer NF, Hyppönen H, Nykänen P, et al. Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications. Yearb Med Inform. 2019;28(01):128–34.

Maier D, Waldherr A, Miltner P, Wiedemann G, Niekler A, Keinert A, et al. Applying LDA topic modeling in communication research: toward a valid and reliable methodology. Commun Methods Meas. 2018;12(2–3):93–118.

Mårtensson G, Ferreira D, Granberg T, Cavallin L, Oppedal K, Padovani A, et al. The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study. Med Image Anal. 2020;66:101714.

Mendo IR, Marques G, de la Torre DI, López-Coronado M, Martín-Rodríguez F. Machine learning in medical emergencies: a systematic review and analysis. J Med Syst. 2021;45(10):1–16.

Miah SJ. An ontology based design environment for rural business decision support. Nathan: Griffith University Nathan; 2008.

Miah SJ, A new semantic knowledge sharing approach for e-government systems. 4th IEEE International Conference on Digital Ecosystems and Technologies; 2010: IEEE.

Miah SJ, Camilleri E, Vu HQ. Big Data in healthcare research: a survey study. J Comput Inf Syst. 2021. https://doi.org/10.1080/08874417.2020.1858727 .

Miah SJ, Gammack J, Kerr D, Ontology development for context-sensitive decision support. Third International Conference on Semantics, Knowledge and Grid (SKG 2007); 2007: IEEE.

Miah SJ, Gammack JG. Ensemble artifact design for context sensitive decision support. Australas J Inf Syst. 2014. https://doi.org/10.3127/ajis.v18i2.898 .

Miah SJ, Gammack JG, McKay J. A metadesign theory for tailorable decision support. J Assoc Inf Syst. 2019;20(5):4.

Mimno D, Blei D, editors. Bayesian checking for topic models. Proceedings of the 2011 conference on empirical methods in natural language processing; 2011.

Oala L, Murchison AG, Balachandran P, Choudhary S, Fehr J, Leite AW, et al. Machine learning for health: algorithm auditing & quality control. J Med Syst. 2021;45(12):1–8.

Ouhbi S, Idri A, Fernández-Alemán JL, Toval A. Requirements engineering education: a systematic mapping study. Requir Eng. 2015;20(2):119–38.

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2020;372:n71.

Quinn KM, Monroe BL, Colaresi M, Crespin MH, Radev DR. How to analyze political attention with minimal assumptions and costs. Am J Polit Sci. 2010;54(1):209–28.

Rowley J, Slack F. Conducting a literature review. Management research news. 2004.

Rozas LW, Klein WC. The value and purpose of the traditional qualitative literature review. J Evid Based Soc Work. 2010;7(5):387–99.

Sabharwal R, Miah SJ. A new theoretical understanding of big data analytics capabilities in organizations: a thematic analysis. J Big Data. 2021;8(1):1–17.

Salazar-Reyna R, Gonzalez-Aleu F, Granda-Gutierrez EM, Diaz-Ramirez J, Garza-Reyes JA, Kumar A. A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems. Management Decision. 2020.

Shah P, Kendall F, Khozin S, Goosen R, Hu J, Laramie J, et al. Artificial intelligence and machine learning in clinical development: a translational perspective. NPJ Digit Med. 2019;2(1):1–5.

Sone D, Beheshti I. Clinical application of machine learning models for brain imaging in epilepsy: a review. Front Neurosci. 2021;15:761.

Spasic I, Nenadic G. Clinical text data in machine learning: systematic review. JMIR Med Inform. 2020;8(3):e17984.

Székely N, Vom Brocke J. What can we learn from corporate sustainability reporting? Deriving propositions for research and practice from over 9,500 corporate sustainability reports published between 1999 and 2015 using topic modelling technique. PLoS ONE. 2017;12(4):e0174807.

Verma D, Bach K, Mork PJ, editors. Application of machine learning methods on patient reported outcome measurements for predicting outcomes: a literature review. Informatics; 2021: Multidisciplinary Digital Publishing Institute.

Weng W-H. Machine learning for clinical predictive analytics. Leveraging data science for global health. Cham: Springer; 2020. p. 199–217.

Book   Google Scholar  

Yin Z, Sulieman LM, Malin BA. A systematic literature review of machine learning in online personal health data. J Am Med Inform Assoc. 2019;26(6):561–76.

Download references

Acknowledgements

Not applicable.

Author information

Authors and affiliations.

Newcastle Business School, The University of Newcastle, Newcastle, NSW, Australia

Renu Sabharwal & Shah J. Miah

You can also search for this author in PubMed   Google Scholar

Contributions

The first author conducted the research, while the second author has ensured quality standards and rewritten the entire findings linking to underlying theories. Both authors read and approved the final manuscript.

Corresponding author

Correspondence to Renu Sabharwal .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests, additional information, publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Sabharwal, R., Miah, S.J. An intelligent literature review: adopting inductive approach to define machine learning applications in the clinical domain. J Big Data 9 , 53 (2022). https://doi.org/10.1186/s40537-022-00605-3

Download citation

Received : 18 November 2021

Accepted : 06 April 2022

Published : 28 April 2022

DOI : https://doi.org/10.1186/s40537-022-00605-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Clinical research
• Systematic literature review

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Healthcare (Basel)

Towards the Use of Big Data in Healthcare: A Literature Review

Associated data.

Not applicable.

The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010–2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills.

1. Introduction

The adoption of Fourth Industrial Revolution technologies, particularly artificial intelligence (AI) and big data (BD), has been a major challenge for all industries [ 1 ]. The increasing technological progress has initiated a digital transformation process in many sectors, including healthcare [ 2 ], which is already moving toward Healthcare 4.0 due to the impact of smart technologies [ 3 , 4 ] such as the Internet of Things (IoT) paradigm [ 5 ], cloud and fog computing [ 5 ], and big data analytics (BDA) [ 6 ].

Healthcare institutions often face many challenges, ranging from epidemics to determining the most suitable therapies for treating diseases. If an AI technology system is applied to medical research, owing to the development, validation, and deployment of various machine learning algorithms for industrial applications with sustainable performance [ 7 ], it has the potential to diagnose, find vaccines, and personalize healthcare services, moving toward highly advanced e-Health [ 8 ].

Patient-centered care cannot ignore the continuous expansion of data in terms of its volume, variety, and velocity, propelling it toward a new technological paradigm, now widely called BD [ 9 , 10 ]. The analysis of the enormous volume, heterogeneity, and velocity of the information provided by BD allows for the extraction of the greatest value from collected data and successfully solving and analyzing the relationships between different variables that describe a patient’s vital functions and that can affect their health [ 11 ]. These data stimulate healthcare organizations to invest heavily in data analysis to facilitate decision-making [ 12 , 13 ]. Integrating data on an individual’s unique characteristics, clinical phenotypes, and biological information obtained from diagnostic imaging to laboratory tests and medical records enables precision medicine to operate under predictive and preventive conditions [ 14 ]. Having abundant data is crucial, especially in critical care environments, to be able to rapidly identify diagnoses and specific treatments for particular or rare pathological cases [ 15 ]. The improvement of critical stages of diagnosis and the personalization of therapeutic treatments for various diseases are spreading rapidly due to the emerging technological development of BD and the use of social media and IoT that allow for collecting various kinds of data generated by a huge number of devices. In particular, these are biomedical sensors and intelligent devices that, during the diagnosis and monitoring of a patient, collect data related to their health and make them accessible through interconnected and integrated systems, facilitating the transmission of information [ 5 , 16 ].

Currently, the health emergency situation caused by the spread of the COVID-19 disease is increasing the need to develop a BD information system for epidemic- and rapid problem-oriented BD acquisition and integration [ 17 , 18 ]. Previous studies examining BD in healthcare predominantly focused on informatics [ 16 , 19 , 20 ] and medical aspects [ 21 , 22 ]. Few studies have analyzed this topic from a managerial point of view [ 23 , 24 ]. Therefore, this paper contributes to this stream of literature by exploring which tangible and intangible elements are needed to draw the maximum benefits from BD.

Scopus has been used to select the most relevant studies on the role of BD in healthcare to generate knowledge even from the most remote contributions to the literature [ 25 ]. The study shows exponential growth in publications, especially since 2020, highlighting the growing criticality and urgency of healthcare and Industry 4.0 integration. Given the increasing interest in BD in healthcare management, a literature review is useful to understand the challenges and opportunities of BD’s use [ 22 ] for future applications in healthcare.

2. Background

BD is a large collection of data from various healthcare sources that enables increasingly personalized treatments, evaluations of their effectiveness, and a reduction of clinical risk through innovative ways of managing and controlling processes [ 26 , 27 , 28 ].

The literature identifies the main features that characterize BD, also known as the 3Vs [ 28 , 29 ]:

• Volume: amount of data generated every second;
• Variety: different types of data generated, accumulated, and used, even unstructured or semi-structured; and
• Velocity: referring to the generation of data (which is always increasing).

To these first 3Vs, another 3Vs were later added [ 30 , 31 ]:

• Veracity or uncertainty of data;
• Value: BD analytics technologies increase the value of data by transforming it into useful information; and
• Variability: data on the same topic can have differences related to their format or mode of collection, and this is often a limitation.

Then a seventh additional characteristic was found:

• Complexity: the larger the size of the dataset, the greater the complexity of the data to be managed.

Although the importance of BD has emerged particularly in finance, banking, and insurance, one of the most promising and interesting areas in which it can effect significant change is healthcare, although its adoption has been slow [ 32 , 33 ]. Global BD in the healthcare market was expected to grow at a CAGR (Compounded Average Growth Rate) of 20.69% between 2015 and 2021 [ 34 ]. BD is transforming the way healthcare is managed, enabling a revolution in knowledge management and data analytics [ 35 ]. The analysis of the large amount of data generated by a single patient related to diagnosis, treatment pathways, drugs, medical devices, digital images, and laboratory analysis results, to be meaningful, requires that these data be validated, processed, and integrated into processing systems that allow for creating new value in the organization of health services [ 36 ]. The data, therefore, are stored in databases and are efficiently managed [ 9 , 37 ], providing useful insights that otherwise would not have been possible and identifying better solutions in terms of health quality and timely decisions [ 23 , 38 ].

The increasing influence of BD has prompted healthcare organizations to use AI and the skills needed to effectively exploit BDA [ 1 , 24 ]. The quantity of data to manage, analyze, and archive is so large and complex that traditional methods of data processing are inadequate [ 39 ]. The potential acquisition and analysis of BD, in fact, requires the restructuring of the technological infrastructure and integration of traditional data analytical tools and techniques with computational technology that is able to enhance and extract information that will be useful for decision-making [ 2 , 36 , 40 ].

The most relevant sources from which to acquire BD in healthcare are medical recordings (e.g., electronic healthcare records, clinical decision support systems, biomedical data, etc.) [ 41 ] as well as external data sources (laboratories, pharmacies, patient-reported data, biometric and other data received directly from patients, etc.). Additional data sources are increasingly available, such us data derived from Internet use (social media) and smart applications [ 42 , 43 , 44 , 45 ]. For the management and processing of these data, many healthcare sectors have adopted cloud computing. It is a solution for receiving and storing huge amounts of patient data and managing electronic medical records [ 46 ]. These heterogeneous data, when properly integrated with the most relevant health data, allow for the monitoring of patients’ health status in various contexts (hospitals, nursing homes, private homes) [ 5 , 16 , 47 ]. This is an aspect of crucial relevance because the main errors that can lead to a misdiagnosis and case fatality occur due to improper monitoring and administration of therapeutic treatment, as well as to drug non-adherence [ 48 ]. Integrating data on an individual’s unique characteristics, clinical phenotypes, and biological information obtained from imaging to laboratory tests and medical records enables individualized diagnostic or therapeutic solutions [ 14 ].

However, there are still many challenges to be faced. The use of cloud computing and other BD analysis tools and techniques in general, in fact, encounters a number of difficulties represented by network failures, security and privacy issues of patient data, and network downtime [ 46 , 49 , 50 ].

The proliferation of increasingly fast network infrastructures is a phenomenon that is directly proportional to the expansion of possibilities of conveying and exchanging health information, opening up scenarios that were unimaginable until a few years ago. However, today, medicine and scientific research in the medical field are no longer carried out with traditional devices but also, for example, through the so-called smart devices that are increasingly becoming essential elements of daily life [ 51 ].

A product of the technological revolution that began prior to 2000 with the explosion of the Internet, and later with the huge spread of new generation devices connected to it (IoT), is e-Health. In fact, e-Health has huge potential for improving the efficiency of the health system (cutting costs) and effectiveness in the management of patients (understood as the quality of healthcare) [ 52 , 53 ]. The convergence toward a health system, Healthcare 4.0 [ 3 , 4 ] based on smart technologies, IoT [ 5 ], data sharing between different actors [ 6 ], robotics, and cloud computing [ 54 ] can lead to improved healthcare delivery. These IoT devices and sensors also play an essential role in analyzing and predicting new diseases, such as COVID-19 [ 55 ].

The affirmation of new technologies has determined the creation of the Digital Imaging and Communication in Medicine (DICOM) standard, which defines the rules for the storage and sharing of images, going beyond the old generation of analog machines. Another AI application relates to digital reporting techniques—that is, electronic health records (EHR), which in a few years will replace paper media [ 56 ]. To protect patient privacy, EHR must be stored as sensitive information in a secure and reliable manner [ 57 ].

The main health benefits of BD are found in disease prevention, in identifying the main health risk factors, and in designing more effective healthcare measures [ 15 , 58 , 59 ]. The rational use of information and communications technology (ICT) represents a revitalization lever for health systems challenged especially during the COVID-19 health emergency. Enhancing decision-making and operational capabilities, reducing errors, and saving resources are the key benefits. In this view, BD is proving to be an important source with new characteristics, potential, and limitations [ 60 , 61 ].

BD and AI technologies have high predictive capabilities in their application in the treatment of cardiovascular diseases. Studies [ 62 , 63 ] have shown that it is mainly machine learning techniques (k-nearest neighbor, decision tree, linear regression, and support vector machine [SVM]) that improve the accuracy of heart disease detection.

Combinations of machine learning methods with deep learning approaches also enhance the use of neuroimaging data to classify and predict Alzheimer’s disease [ 64 ]. Alzheimer’s is a degenerative neurological disease that impairs a person’s ability to function independently, making early diagnosis critical. Sharma et al. proposed a Hadoop-based BD system for early indicators of the disease. Such a system involves combining data obtained from noninvasive magnetic resonance imaging (MRI), spectrography, magnetic resonance spectroscopy (MRS), and neuropsychological test results [ 65 ]. Kautzky et al. (2018), however, developed a prediction model based on a single diagnostic factor that allows early detection of brain abnormalities even before the onset of symptoms [ 66 ].

An additional degenerative disease in which to employ machine learning is Parkinson’s disease, which affects the neurological system and limits mobility. For early detection of disease symptoms, some studies have used k-nearest neighbor, random forest, and decision tree algorithms [ 67 , 68 ]. Sivaparthipan et al. (2019) also highlighted the importance of data collection using cell phones to recognize the gait of Parkinson’s patients [ 69 ].

Different machine learning (ML) techniques are also used to improve the prediction results for cancer, a major cause of mortality globally [ 70 ]. Torkey et al. (2021) proposed two survival prediction models based on deep learning that can guide physicians in determining breast cancer treatment options and avoid ineffective treatments [ 71 ]. In another study, Torkey et al. (2021) used an ML model that, through the construction of a DNA microarray dataset, allows for the identification of discriminative features that influence the classification of different kinds of cancer and facilitate their early diagnosis [ 72 ].

3. Materials and Methods

A systematic literature review was conducted over the period 2010–2021 to explore the main areas of application of BD in healthcare and the organizational changes needed to address the challenges of applying BD in this area, as well as to illustrate the potential benefits in light of the COVID-19 health emergency that, with its extemporaneity and unpredictability, has severely affected the healthcare management [ 73 ]. A review was also conducted of works from 2022, considering that the scientific production on the investigated topic is already significant.

To ensure a transparent and high-quality process, the analysis comprised four phases [ 68 ]:

• Planning and identification of studies
• Article evaluation
• Extraction of results
• Summary and dissemination of audit results

The analysis was carried out using the Scopus database [ 74 ], and the articles were selected by searching for both “Big Data” and “Healthcare” in the title, abstract, or keywords of an article.

The research conducted, without any restriction on the type of contribution, was delimited with respect to the year of publication and the research area of business, management, and accounting. Subsequently, screening was carried out to assess suitability with respect to the inclusion criteria, first analyzing the relevance of the title, abstract, and then the full text of an article [ 36 ]. Works that were not directly related to the definition, process, and use of BD in healthcare management were excluded. Finally, the remaining 93 papers met all inclusion criteria.

Table 1 shows the steps followed in the search strategy:

Review Strategy.

Table 2 shows the journals that have published the most articles. In particular, International Journal Recent Technology and Engineering presents the highest number of publications (25), covering articles in the areas of computer science and engineering; information technology; electrical and electronics engineering; telecommunication; mechanical, civil, and textile engineering; and all interdisciplinary streams of engineering sciences. This is followed by International Journal of Scientific & Technology Research with 11 publications and Lecture Notes in Business Information Processing with 10 papers in the fields of engineering, science, technology, and industrial application software development. There are many other journals with one paper each.

Top five sources.

Applications of BDA in healthcare are gradually increasing with the growing volume of BD in this context since 2014, with new research areas evolving and applications being explored ( Figure 1 ).

Annual distribution of publications.

The recent literature regarding BD in healthcare discusses the following three themes:

• - BD and health awareness
• - BD and digital transformation
• - BD and analytical skills

This section presents the results of the analysis, highlighting the benefits, challenges, and risks of BD’s use in the healthcare sector.

4.1. Big Data and Health Awareness

The main health benefits of BD are found in disease prevention, in identifying the main health risk factors and in designing more effective healthcare measures [ 58 , 59 , 75 ]. BD, in fact, supports the digitization of all medical records by making all data related to each patient’s medical history available [ 76 ].

The areas that could gain more than others from the benefits of technology and, in particular, from the preservation and sharing of large amounts of clinical data are predictivity, timely diagnosis, and personalized treatment, also favoring the development of precision medicine (patient-centered care) [ 38 , 39 ]. Taking advantage of new biotechnological discoveries allows for going beyond the traditional concept of a “standard patient” to treating the “individual” in their uniqueness [ 77 , 78 ], owing to the analysis of interactions between the different variables that describe the patient’s vital functions and that may affect their health [ 11 ], with enormous benefits for medical functions [ 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ]. This improves efficiency and is more patient-centric, yielding consistent, suitable, safe, and flexible solutions [ 39 , 80 ] due to the vigorous analysis by applying various machine learning techniques [ 2 ].

The predictive use of BDA tools allows, especially in cases of health emergencies, for the prompt reporting of high-risk patients and ensuring more effective and efficient care, thus improving overall healthcare outcomes [ 9 , 81 , 82 ]. In fact, the heterogeneity, volume, and velocity of the data contribute to the monitoring of population flows and trends, which are of crucial importance both for early diagnoses and for personalized healthcare services [ 8 , 12 , 13 ]. El Samad et al. (2021), in this regard, conducted a study showing that BD management is considered a key prerogative for the quality of medical services and conditions [ 83 ].

AI-based diagnosis systems and algorithms to detect new outbreaks are just some of the tools that could limit the spread of the SARS-CoV-2 virus and related disease COVID-19, thus maximizing healthcare resources [ 13 , 17 , 84 ] and contributing to the containment of pandemic risk on national territory [ 85 , 86 ]. Abdel-Basset et al. (2021) demonstrated the relevant role that disruptive technologies for COVID-19 analysis, such as AI, Industry 4.0, IoT, Internet of Medical Things (IoMT), BD, virtual reality (VR), drone technology and autonomous robots, 5G, and blockchain, have played in limiting the spread of COVID-19 outbreaks [ 87 ]. Another technology that is being widely deployed is Wireless Body Area Networks (WBANs). This is an innovative solution that can restructure healthcare and make pervasive support available to patients [ 87 , 88 ].

The evolution of digital healthcare into mobile healthcare (mobile health) has also made it possible to manage information via apps that a patient can download directly to their smartphone or tablet [ 89 ], allowing them to monitor their health status independently and share it with their doctor [ 44 , 90 ]. Therefore, IoT allows a much better and timely diagnosis of the patient’s status and offers medical services via telemedicine, even in remote locations [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ] still underdeveloped, where the number of specialists in health services is insufficient [ 92 ]. The use of telemedicine is of fundamental importance in this SARS-CoV-2 pandemic period for preventing and managing COVID-19 infection [ 93 ].

The many applications of medical IoT in the field of monitoring include sensors to monitor vital parameters, such as blood pressure, heart rate, etc.; smart tags (i.e., chips inserted in clothing) with monitoring and data scanning functions; bracelets or other wearable devices capable of detecting vital parameters and forwarding emergency calls in case of anomalies; and Real-Time Location System (RTLS) (i.e., Global Positioning System [GPS]), which is a satellite-based navigation system used to locate ambulances, patients, doctors, etc. [ 5 , 9 ].

Most of these systems are set for patients suffering from dementia [ 94 ] or diabetes [ 95 ] and with vascular pathologies [ 96 ].

The combination and wise and competent use of these BD sources can help health operators undertaking various individual or collective activities, summarized in precision medicine, predictive medicine, and prevention.

Fanta et al. (2021) argued that digital technologies are also a supporting tool in the healthcare sector’s transition to a circular economy. Indeed, such tools, and IoT in particular, can support the collection of end-of-life healthcare products as well as their recycling, regeneration, and disposal [ 97 , 98 ].

Table 3 shows top ten articles per citations on theme “Big Data and awareness”.

Top ten articles per citations on theme 1: Big Data and awareness.

4.2. Big Data and Digital Transformation

The literature review showed that the digital health phenomenon is a true paradigm of innovation that allows for increasing the quality of health services and shaping them according to the needs of the patient, proceeding to the control of their health in real time regardless of geographical location [ 99 , 100 ]. It also requires the main political, legal, and medical players to reconsider the risks associated with the processing of data in the health sector; promote a cultural change, even more than an organizational one, in digital transformation; and investigate new protocols for a more efficient and secure transmission of sensitive health data [ 97 , 98 , 99 , 100 , 101 ].

To manage data in a structured way and address the privacy system effectively and more robustly, healthcare organizations are looking for AI and analytics techniques that will enable them to consolidate organizational resources and develop new data-driven and integrated governance [ 102 ]. Managing an integrated healthcare solution requires security of medical data, which can be achieved with the cryptosystem, which has been found to be highly secure against attacks and interference [ 103 ]. The goal is to allow the development of ways to monitor the health conditions of the population using a huge volume, variety, and velocity of data from a wide range of healthcare networks in an aggregate and anonymous form [ 104 , 105 , 106 ] and improve its performance.

The optimal use of resources has a critically important role for healthcare operators in assessing the quality of the healthcare service provided and also requires appropriate technologies to ensure the rational use of resources [ 107 , 108 , 109 ]. Benzidia et al. (2021) claim that extracting new insights from existing volumes of structured and unstructured data related to medical treatments and products improves decision-making and enables a better understanding of each patient’s costs [ 93 , 110 ].

The result may be an important analytical capability of BD through the definition of previously unobserved patterns and improved resource efficiency through the identification of costly healthcare services, such as unnecessary diagnostic tests and additional treatments [ 19 , 111 ].

Introducing advanced digital solutions to explore huge amounts of heterogeneous and unstructured data requires the design of a clear and integrated strategy across all areas of innovation. It is appropriate to start with understanding the actual level of digital maturity to explore its potential benefits driven by BD analytics and to create value for their healthcare organizations [ 112 , 113 ].

Ultimately, healthcare organizations must begin to develop a concrete analysis of how to apply emerging technologies to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, patient diagnose, and epidemic forecasting [ 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. In this way, risks can be minimized and decisions can be made from the perspective of improved effectiveness and efficiency [ 114 , 115 ].

To accelerate digitalization, hospitals must invest in technology to automate processes and streamline operations, moving in two distinct directions: focusing on the organizational level (moving from episodic to coordinated care), where telemedicine is prioritized, and introducing digital solutions to enable new models of care (progressing toward personalized care and increasing the focus on prevention and wellness) [ 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 ].

Implementing a Healthcare 4.0 system requires the careful consideration of process improvement as part of the overall plan to achieve maximum benefits from technology adoption [ 117 ]. In the absence of a strategy that indicates a precise and coherent direction for the evolution of organizational and technological models, it is easy to get lost within an ever-increasing range of technologies, and perhaps end up choosing and introducing advanced digital solutions in an organizational context that is unable to grasp all the advantages to transform the competitive landscape and improve organizational performance [ 52 ]. New technologies enable the creation of high-quality datasets and extract value from them [ 118 , 119 ] but with a rethinking of existing business models [ 120 , 121 ].

Table 4 shows top ten articles per citations on theme “Big Data and digital transformation”.

Top ten articles per citations on theme 2: Big Data and digital transformation.

4.3. Big Data and Analytical Skills

BD can offer a major competitive advantage for healthcare providers, especially with regard to reducing therapeutic mistakes by analyzing patient data [ 120 , 122 ]. In fact, software that uses BD efficiently can flag any inconsistencies between a patient’s medical history and their drug allergies with the medications they are taking, thus alerting the referring physician to discontinue therapy. BD can also identify chronically ill citizens, providing them with preventive actions to avoid clogging emergency channels, such as the emergency room [ 117 , 118 , 123 ].

To seize these important opportunities, however, it is necessary to make a considerable investment in healthcare organizations, for example, in the hiring of analytics experts—that is, professionals capable of identifying problems from the data and proposing the most appropriate solutions [ 23 , 124 , 125 ]. Gravili et al. (2021) highlighted the crucial role of intangible elements, especially of the intellectual capital in the health sector. The dissemination of new knowledge and specialized skills promotes the sharing of best practices in the health sector. The result is a reduction in mortality rates, better outcomes in terms of cost minimization, and a reduction in hospitalization periods [ 115 , 126 , 127 ]. De Mauro et al. (2018) proposed a classification of job roles and skill sets needed in the BD and AI era. This classification provides valuable support for business leaders and human resource managers in the selection process and in developing the skills needed to make the most of BD [ 72 , 128 ].

The management of a highly variable amount of data in real time requires not only new tools and methods but also the development of new knowledge and skills that are essential for converting data into a strategic resource and for implementing new management practices or a new organizational culture across the entire organization. A lack of data analytics skills among existing employees may increase data entry errors that could result in placing information in the wrong record, losing valuable information, and limiting the value a business can derive from the data that it captures [ 129 ]. BD analysis is essential in defining the patient diagnosis. Therefore, doctors and nurses’ understanding of data undoubtedly has a positive impact on the rapid recovery of patients in hospitals [ 62 , 63 ]. These are professionals with technical skills and multidisciplinary knowledge who can manage a huge volume of data and extract useful information to ensure adequate social and healthcare and support the restructuring of healthcare processes [ 64 , 65 ].

Furthermore, process innovation and efficient scheduling are key to addressing bottlenecks in healthcare management [ 64 , 119 ].

Table 5 shows top ten articles per citations on theme 3 “Big Data and analytical skills”.

Top ten articles per citations on theme 3: Big Data and analytical skills.

5. Discussion and Conclusions

In recent years, there has been a process of digitalization and technological innovation in the healthcare sector to enable the transformation of a huge volume of data into valuable health BD, optimize resources, and improve both the patient experience and organizational performance [ 66 ]. The main sources of health data are EHR [ 38 ], medical data [ 130 ], laboratory information systems [ 131 , 132 , 133 , 134 , 135 ], biometric sources, patient-reported data, and social media (wearable devices and sensors that provide information about a patient’s lifestyle) [ 22 , 130 ].

The rapid deployment of new emergency devices (i.e., wireless communications, mobile computing, and mobile devices) and patient monitoring systems has allowed for the focus to be on the design and delivery of digital health services that, leveraging real-time data, foster integrated and effective governance. It is essential to ensure a personalized health service, early disease diagnosis, and support for patient undergoing online care treatments [ 132 ]. The gradual implementation of advanced digital solutions will support the clinical team’s decisions and release time for the most value-added clinical activities and treatment of the most complex cases. BD and AI not only have great potential in the fight against infectious diseases but can also be used for rapid drug and vaccine development [ 130 ].

Despite the important strides made in healthcare digitalization, there are numerous challenges to making the healthcare sector more resilient in the face of health crises. In this regard, it is necessary not only to strengthen the system but also to change its architecture toward a connected care model in which the organization, care, and assistance processes are redefined from a digital perspective and allow for making informed decisions using cutting-edge technology and BDA [ 22 , 134 , 135 ].

The transformation in health information acquisition and informed decision-making using cutting-edge technologies, however, must compromise with the mitigation of privacy associated with patient risks and data confidentiality protection [ 131 , 132 ]. The COVID-19 health emergency has illuminated the need for the careful consideration of the evolving relationship between privacy and public health and the relevance of the public interest in personal data processing activities [ 18 ]. These exceptional and contingent circumstances have highlighted the importance of data protection regulations and cybersecurity investment plans aimed at channeling the flow of BD into healthcare. Indeed, the collection and use of health-related data have been indispensable tools in the effort to counter and contain the pandemic [ 17 ].

Moreover, the evolution of technologies and the competitive environment require the development of new skills in the field of data analysis. In the Fourth Industrial Revolution, people continue to be the most strategic and important component in the business, and it is becoming increasingly strategic to be able to acquire analytical skills to analyze and transform consolidated data from existing fragmented data sources into valuable information for business decision-makers. In this way, it is possible to gain a competitive advantage through timely and more informed decisions based on adequate knowledge of descriptive analytics and predictive analytics, analytical techniques that are ideal for analyzing a large proportion of text-based health documents and other unstructured clinical data [ 135 ].

In conclusion, personalization of care, reduction of hospitalization, and effectiveness and cost containment of services and waiting lists are benefits unquestionably linked to digitalization and technological innovation but that require a review of the systems of traceability and control with a revolution of traditional ICT systems.

This is the challenge that healthcare must overcome. In fact, over the years, analyzing these data and sharing the results with managers and healthcare operators has made it possible to improve the level of knowledge of the system, the sustainability of the healthcare system, its accountability and transparency, and the quality and equity of care.

Our work has theoretical and practical implications. From the theoretical perspective, the paper, by proposing a literature review with a strong focus on managerial aspects, extends the literature by enriching a growing field of research. From the practical perspective, the paper reveals the need to develop new skills and redesign operational and strategic processes to consciously use heterogeneous data in future scenarios.

This paper presents several limitations. First, we used a defined set of keywords and only one database. Second, the research was conducted without programs like VOSviewer that could be used in future studies to identify new clusters related to this topic.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, G.G. and G.D.; methodology, M.S.; validation, G.D. and A.M.; formal analysis, G.G.; investigation, G.G.; data curation, M.S.; writing—original draft preparation, G.G.; writing—review and editing, A.M. and G.G.; visualization, M.S.; supervision, G.D. and A.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

IEEE Account

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Accelerate your research with the best systematic literature review tools

The ideal literature review tool helps you make sense of the most important insights in your research field. ATLAS.ti empowers researchers to perform powerful and collaborative analysis using the leading software for literature review.

Finalize your literature review faster with comfort

ATLAS.ti makes it easy to manage, organize, and analyze articles, PDFs, excerpts, and more for your projects. Conduct a deep systematic literature review and get the insights you need with a comprehensive toolset built specifically for your research projects.

Figure out the "why" behind your participant's motivations

Understand the behaviors and emotions that are driving your focus group participants. With ATLAS.ti, you can transform your raw data and turn it into qualitative insights you can learn from. Easily determine user intent in the same spot you're deciphering your overall focus group data.

Visualize your research findings like never before

We make it simple to present your analysis results with meaningful charts, networks, and diagrams. Instead of figuring out how to communicate the insights you just unlocked, we enable you to leverage easy-to-use visualizations that support your goals.

Paper Search – Access 200+ Million Papers with AI-Powered Insights

Unlock access to over 200 million scientific papers and streamline your research with our cutting-edge AI-powered Paper Search 2.0. Easily find, summarize, and integrate relevant papers directly into your ATLAS.ti Web workspace.

Everything you need to elevate your literature review

Import and organize literature data.

Import and analyze any type of text content – ATLAS.ti supports all standard text and transcription files such as Word and PDF.

Analyze with ease and speed

Utilize easy-to-learn workflows that save valuable time, such as auto coding, sentiment analysis, team collaboration, and more.

Leverage AI-driven tools

Make efficiency a priority and let ATLAS.ti do your work with AI-powered research tools and features for faster results.

Visualize and present findings

With just a few clicks, you can create meaningful visualizations like charts, word clouds, tables, networks, among others for your literature data.

The faster way to make sense of your literature review. Try it for free, today.

A literature review analyzes the most current research within a research area. A literature review consists of published studies from many sources:

• Peer-reviewed academic publications
• Full-length books
• University bulletins
• Conference proceedings
• Dissertations and theses

Literature reviews allow researchers to:

• Summarize the state of the research
• Identify unexplored research inquiries
• Recommend practical applications
• Critique currently published research

Literature reviews are either standalone publications or part of a paper as background for an original research project. A literature review, as a section of a more extensive research article, summarizes the current state of the research to justify the primary research described in the paper.

For example, a researcher may have reviewed the literature on a new supplement's health benefits and concluded that more research needs to be conducted on those with a particular condition. This research gap warrants a study examining how this understudied population reacted to the supplement. Researchers need to establish this research gap through a literature review to persuade journal editors and reviewers of the value of their research.

Consider a literature review as a typical research publication presenting a study, its results, and the salient points scholars can infer from the study. The only significant difference with a literature review treats existing literature as the research data to collect and analyze. From that analysis, a literature review can suggest new inquiries to pursue.

Identify a focus

Similar to a typical study, a literature review should have a research question or questions that analysis can answer. This sort of inquiry typically targets a particular phenomenon, population, or even research method to examine how different studies have looked at the same thing differently. A literature review, then, should center the literature collection around that focus.

Collect and analyze the literature

With a focus in mind, a researcher can collect studies that provide relevant information for that focus. They can then analyze the collected studies by finding and identifying patterns or themes that occur frequently. This analysis allows the researcher to point out what the field has frequently explored or, on the other hand, overlooked.

Suggest implications

The literature review allows the researcher to argue a particular point through the evidence provided by the analysis. For example, suppose the analysis makes it apparent that the published research on people's sleep patterns has not adequately explored the connection between sleep and a particular factor (e.g., television-watching habits, indoor air quality). In that case, the researcher can argue that further study can address this research gap.

External requirements aside (e.g., many academic journals have a word limit of 6,000-8,000 words), a literature review as a standalone publication is as long as necessary to allow readers to understand the current state of the field. Even if it is just a section in a larger paper, a literature review is long enough to allow the researcher to justify the study that is the paper's focus.

Note that a literature review needs only to incorporate a representative number of studies relevant to the research inquiry. For term papers in university courses, 10 to 20 references might be appropriate for demonstrating analytical skills. Published literature reviews in peer-reviewed journals might have 40 to 50 references. One of the essential goals of a literature review is to persuade readers that you have analyzed a representative segment of the research you are reviewing.

Researchers can find published research from various online sources:

• Journal websites
• Research databases
• Search engines (Google Scholar, Semantic Scholar)
• Research repositories
• Social networking sites (Academia, ResearchGate)

Many journals make articles freely available under the term "open access," meaning that there are no restrictions to viewing and downloading such articles. Otherwise, collecting research articles from restricted journals usually requires access from an institution such as a university or a library.

Evidence of a rigorous literature review is more important than the word count or the number of articles that undergo data analysis. Especially when writing for a peer-reviewed journal, it is essential to consider how to demonstrate research rigor in your literature review to persuade reviewers of its scholarly value.

Select field-specific journals

The most significant research relevant to your field focuses on a narrow set of journals similar in aims and scope. Consider who the most prominent scholars in your field are and determine which journals publish their research or have them as editors or reviewers. Journals tend to look favorably on systematic reviews that include articles they have published.

Incorporate recent research

Recently published studies have greater value in determining the gaps in the current state of research. Older research is likely to have encountered challenges and critiques that may render their findings outdated or refuted. What counts as recent differs by field; start by looking for research published within the last three years and gradually expand to older research when you need to collect more articles for your review.

Consider the quality of the research

Literature reviews are only as strong as the quality of the studies that the researcher collects. You can judge any particular study by many factors, including:

• the quality of the article's journal
• the article's research rigor
• the timeliness of the research

The critical point here is that you should consider more than just a study's findings or research outputs when including research in your literature review.

Narrow your research focus

Ideally, the articles you collect for your literature review have something in common, such as a research method or research context. For example, if you are conducting a literature review about teaching practices in high school contexts, it is best to narrow your literature search to studies focusing on high school. You should consider expanding your search to junior high school and university contexts only when there are not enough studies that match your focus.

You can create a project in ATLAS.ti for keeping track of your collected literature. ATLAS.ti allows you to view and analyze full text articles and PDF files in a single project. Within projects, you can use document groups to separate studies into different categories for easier and faster analysis.

For example, a researcher with a literature review that examines studies across different countries can create document groups labeled "United Kingdom," "Germany," and "United States," among others. A researcher can also use ATLAS.ti's global filters to narrow analysis to a particular set of studies and gain insights about a smaller set of literature.

ATLAS.ti allows you to search, code, and analyze text documents and PDF files. You can treat a set of research articles like other forms of qualitative data. The codes you apply to your literature collection allow for analysis through many powerful tools in ATLAS.ti:

• Code Co-Occurrence Explorer
• Code Co-Occurrence Table
• Code-Document Table

Other tools in ATLAS.ti employ machine learning to facilitate parts of the coding process for you. Some of our software tools that are effective for analyzing literature include:

• Named Entity Recognition
• Opinion Mining
• Sentiment Analysis

As long as your documents are text documents or text-enable PDF files, ATLAS.ti's automated tools can provide essential assistance in the data analysis process.

Literature Review with MAXQDA

Interview transcription examples, make the most out of your literature review.

Literature reviews are an important step in the data analysis journey of many research projects, but often it is a time-consuming and arduous affair. Whether you are reviewing literature for writing a meta-analysis or for the background section of your thesis, work with MAXQDA. Our product comes with many exciting features which make your literature review faster and easier than ever before. Whether you are a first-time researcher or an old pro, MAXQDA is your professional software solution with advanced tools for you and your team.

How to conduct a literature review with MAXQDA

Conducting a literature review with MAXQDA is easy because you can easily import bibliographic information and full texts. In addition, MAXQDA provides excellent tools to facilitate each phase of your literature review, such as notes, paraphrases, auto-coding, summaries, and tools to integrate your findings.

Step one: Plan your literature review

Similar to other research projects, one should carefully plan a literature review. Before getting started with searching and analyzing literature, carefully think about the purpose of your literature review and the questions you want to answer. This will help you to develop a search strategy which is needed to stay on top of things. A search strategy involves deciding on literature databases, search terms, and practical and methodological criteria for the selection of high-quality scientific literature.

MAXQDA supports you during this stage with memos and the newly developed Questions-Themes-Theories tool (QTT). Both are the ideal place to store your research questions and search parameters. Moreover, the Question-Themes-Theories tool is perfectly suited to support your literature review project because it provides a bridge between your MAXQDA project and your research report. It offers the perfect enviornment to bring together findings, record conclusions and develop theories.

Step two: Search, Select, Save your material

Follow your search strategy. Use the databases and search terms you have identified to find the literature you need. Then, scan the search results for relevance by reading the title, abstract, or keywords. Try to determine whether the paper falls within the narrower area of the research question and whether it fulfills the objectives of the review. In addition, check whether the search results fulfill your pre-specified eligibility criteria. As this step typically requires precise reading rather than a quick scan, you might want to perform it in MAXQDA. If the piece of literature fulfills your criteria and context, you can save the bibliographic information using a reference management system which is a common approach among researchers as these programs automatically extract a paper’s meta-data from the publishing website. You can easily import this bibliographic data into MAXQDA via a specialized import tool. MAXQDA is compatible with all reference management programs that are able to export their literature databases in RIS format which is a standard format for bibliographic information. This is the case with all mainstream literature management programs such as Citavi, DocEar, Endnote, JabRef, Mendeley, and Zotero.

Step three: Import & Organize your material in MAXQDA

Importing bibliographic data into MAXQDA is easy and works seamlessly for all reference management programs that use the standard RIS files. MAXQDA offers an import option dedicated to bibliographic data which you can find in the MAXQDA Import tab. To import the selected literature, just click on the corresponding button, select the data you want to import, and click okay. Upon import, each literature entry becomes its own text document. If full texts are imported, MAXQDA automatically connects the full text to the literature entry with an internal link. The individual information in the literature entries is automatically coded for later analysis so that, for example, all titles or abstracts can be compiled and searched. To help you keeping your literature (review) organized, MAXQDA automatically creates a document group called “References” which contains the individual literature entries. Like full texts or interview documents, the bibliographic entries can be searched, coded, linked, edited, and you can add memos for further qualitative and quantitative content analysis (Kuckartz & Rädiker, 2019). Especially, when running multiple searches using different databases or search terms, you should carefully document your approach. Besides being a great place to store the respective search parameters, memos are perfectly suited to capture your ideas while reviewing our literature and can be attached to text segments, documents, document groups, and much more.

Analyze your literature with MAXQDA

Once imported into MAXQDA, you can explore your material using a variety of tools and functions. With MAXQDA as your literature review & analysis software, you have numerous possibilities for analyzing your literature and writing your literature review – impossible to mention all. Thus, we can present only a subset of tools here. Check out our literature about performing literature reviews with MAXQDA to discover more possibilities.

AI Assist: Introducing AI to literature reviews

AI Assist – MAXQDA’s AI-based add-on module – can simplify your literature reviews in many ways. Chat with your data and ask the AI questions about your documents. Let AI Assist automatically summarize entire papers and text segments. Automatically create summaries of all coded segments of a code or generate suggestions for subcodes, and if you don’t know a word’s or concept’s meaning, use AI Assist to get a definition without leaving MAXQDA. Visit our research guide for even more ideas on how AI can support your literature review:

AI for Literature Review

Code & Retrieve important segments

Coding qualitative data lies at the heart of many qualitative data analysis approaches and can be useful for literature reviews as well. Coding refers to the process of labeling segments of your material. For example, you may want to code definitions of certain terms, pro and con arguments, how a specific method is used, and so on. In a later step, MAXQDA allows you to compile all text segments coded with one (or more) codes of interest from one or more papers, so that you can for example compare definitions across papers.

But there is more. MAXQDA offers multiple ways of coding, such as in-vivo coding, highlighters, emoticodes, Creative Coding, or the Smart Coding Tool. The compiled segments can be enriched with variables and the segment’s context accessed with just one click. MAXQDA’s Text Search & Autocode tool is especially well-suited for a literature review, as it allows one to explore large amounts of text without reading or coding them first. Automatically search for keywords (or dictionaries of keywords), such as important concepts for your literature review, and automatically code them with just a few clicks.

Paraphrase literature into your own words

Another approach is to paraphrase the existing literature. A paraphrase is a restatement of a text or passage in your own words, while retaining the meaning and the main ideas of the original. Paraphrasing can be especially helpful in the context of literature reviews, because paraphrases force you to systematically summarize the most important statements (and only the most important statements) which can help to stay on top of things.

With MAXQDA as your literature review software, you not only have a tool for paraphrasing literature but also tools to analyze the paraphrases you have written. For example, the Categorize Paraphrases tool (allows you to code your parpahrases) or the Paraphrases Matrix (allows you to compare paraphrases side-by-side between individual documents or groups of documents.)

Summaries & Overview tables: A look at the Bigger Picture

When conducting a literature review you can easily get lost. But with MAXQDA as your literature review software, you will never lose track of the bigger picture. Among other tools, MAXQDA’s overview and summary tables are especially useful for aggregating your literature review results. MAXQDA offers overview tables for almost everything, codes, memos, coded segments, links, and so on. With MAXQDA literature review tools you can create compressed summaries of sources that can be effectively compared and represented, and with just one click you can easily export your overview and summary tables and integrate them into your literature review report.

Visualize your qualitative data

The proverb “a picture is worth a thousand words” also applies to literature reviews. That is why MAXQDA offers a variety of Visual Tools that allow you to get a quick overview of the data, and help you to identify patterns. Of course, you can export your visualizations in various formats to enrich your final report. One particularly useful visual tool for literature reviews is the Word Cloud. It visualizes the most frequent words and allows you to explore key terms and the central themes of one or more papers. Thanks to the interactive connection between your visualizations with your MAXQDA data, you will never lose sight of the big picture. Another particularly useful tool is MAXQDA’s word/code frequency tool with which you can analyze and visualize the frequencies of words or codes in one or more documents. As with Word Clouds, nonsensical words can be added to the stop list and excluded from the analysis.

QTT: Synthesize your results and write up the review

MAXQDA has an innovative workspace to gather important visualization, notes, segments, and other analytics results. The perfect tool to organize your thoughts and data. Create a separate worksheet for your topics and research questions, fill it with associated analysis elements from MAXQDA, and add your conclusions, theories, and insights as you go. For example, you can add Word Clouds, important coded segments, and your literature summaries and write down your insights. Subsequently, you can view all analysis elements and insights to write your final conclusion. The Questions-Themes-Theories tool is perfectly suited to help you finalize your literature review reports. With just one click you can export your worksheet and use it as a starting point for your literature review report.

Literature about Literature Reviews and Analysis

We offer a variety of free learning materials to help you get started with your literature review. Check out our Getting Started Guide to get a quick overview of MAXQDA and step-by-step instructions on setting up your software and creating your first project with your brand new QDA software. In addition, the free Literature Reviews Guide explains how to conduct a literature review with MAXQDA in more detail.

Getting Started with MAXQDA

Literature Reviews with MAXQDA

A literature review is a critical analysis and summary of existing research and literature on a particular topic or research question. It involves systematically searching and evaluating a range of sources, such as books, academic journals, conference proceedings, and other published or unpublished works, to identify and analyze the relevant findings, methodologies, theories, and arguments related to the research question or topic.

A literature review’s purpose is to provide a comprehensive and critical overview of the current state of knowledge and understanding of a topic, to identify gaps and inconsistencies in existing research, and to highlight areas where further research is needed. Literature reviews are commonly used in academic research, as they provide a framework for developing new research and help to situate the research within the broader context of existing knowledge.

A literature review is a critical evaluation of existing research on a particular topic and is part of almost every research project. The literature review’s purpose is to identify gaps in current knowledge, synthesize existing research findings, and provide a foundation for further research. Over the years, numerous types of literature reviews have emerged. To empower you in coming to an informed decision, we briefly present the most common literature review methods.

• Narrative Review : A narrative review summarizes and synthesizes the existing literature on a particular topic in a narrative or story-like format. This type of review is often used to provide an overview of the current state of knowledge on a topic, for example in scientific papers or final theses.
• Systematic Review : A systematic review is a comprehensive and structured approach to reviewing the literature on a particular topic with the aim of answering a defined research question. It involves a systematic search of the literature using pre-specified eligibility criteria and a structured evaluation of the quality of the research.
• Meta-Analysis : A meta-analysis is a type of systematic review that uses statistical techniques to combine and analyze the results from multiple studies on the same topic. The goal of a meta-analysis is to provide a more robust and reliable estimate of the effect size than can be obtained from any single study.
• Scoping Review : A scoping review is a type of systematic review that aims to map the existing literature on a particular topic in order to identify the scope and nature of the research that has been done. It is often used to identify gaps in the literature and inform future research.

There is no “best” way to do a literature review, as the process can vary depending on the research question, field of study, and personal preferences. However, here are some general guidelines that can help to ensure that your literature review is comprehensive and effective:

• Carefully plan your literature review : Before you start searching and analyzing literature you should define a research question and develop a search strategy (for example identify relevant databases, and search terms). A clearly defined research question and search strategy will help you to focus your search and ensure that you are gathering relevant information. MAXQDA’s Questions-Themes-Theories tool is the perfect place to store your analysis plan.
• Evaluate your sources : Screen your search results for relevance to your research question, for example by reading abstracts. Once you have identified relevant sources, read them critically and evaluate their quality and relevance to your research question. Consider factors such as the methodology used, the reliability of the data, and the overall strength of the argument presented.
• Synthesize your findings : After evaluating your sources, synthesize your findings by identifying common themes, arguments, and gaps in the existing research. This will help you to develop a comprehensive understanding of the current state of knowledge on your topic.
• Write up your review : Finally, write up your literature review, ensuring that it is well-structured and clearly communicates your findings. Include a critical analysis of the sources you have reviewed, and use evidence from the literature to support your arguments and conclusions.

Overall, the key to a successful literature review is to be systematic, critical, and comprehensive in your search and evaluation of sources.

As in all aspects of scientific work, preparation is the key to success. Carefully think about the purpose of your literature review, the questions you want to answer, and your search strategy. The writing process itself will differ depending on the your literature review method. For example, when writing a narrative review use the identified literature to support your arguments, approach, and conclusions. By contrast, a systematic review typically contains the same parts as other scientific papers: Abstract, Introduction (purpose and scope), Methods (Search strategy, inclusion/exclusion characteristics, …), Results (identified sources, their main arguments, findings, …), Discussion (critical analysis of the sources you have reviewed), Conclusion (gaps or inconsistencies in the existing research, future research, implications, etc.).

Start your free trial

Your trial will end automatically after 14 days and will not renew. There is no need for cancelation.

• RIT Libraries
• Data Analytics Resources
• Literature Review
• Electronic Books
• Print Books
• Data Science: Journals
• More Journals, Websites
• Alerts, IDS Express
• Readings on Data
• Sources with Data
• Google Scholar Library Links
• Zotero-Citation Management Tool
• Writing a Literature Review
• ProQuest Research Companion
• Writing a Research Proposal
• Thesis Submission Instructions
• Associations

Edit this Guide

Log into Dashboard

Use of RIT resources is reserved for current RIT students, faculty and staff for academic and teaching purposes only. Please contact your librarian with any questions.

Help is Available

Email a Librarian

A librarian is available by e-mail at [email protected]

Meet with a Librarian

Call reference desk voicemail.

A librarian is available by phone at (585) 475-2563 or on Skype at llll

Or, call (585) 475-2563 to leave a voicemail with the reference desk during normal business hours .

Chat with a Librarian

Data analytics resources infoguide url.

https://infoguides.rit.edu/DA

Use the box below to email yourself a link to this guide

Literature Reviews

• Getting started

What is a literature review?

Why conduct a literature review, stages of a literature review, lit reviews: an overview (video), check out these books.

• Types of reviews
• 1. Define your research question
• 2. Plan your search
• 3. Search the literature
• 4. Organize your results
• 5. Synthesize your findings
• 6. Write the review
• Artificial intelligence (AI) tools
• Thompson Writing Studio This link opens in a new window
• Need to write a systematic review? This link opens in a new window

Guide Owner

Contact a Librarian

Ask a Librarian

Definition: A literature review is a systematic examination and synthesis of existing scholarly research on a specific topic or subject.

Purpose: It serves to provide a comprehensive overview of the current state of knowledge within a particular field.

Analysis: Involves critically evaluating and summarizing key findings, methodologies, and debates found in academic literature.

Identifying Gaps: Aims to pinpoint areas where there is a lack of research or unresolved questions, highlighting opportunities for further investigation.

Contextualization: Enables researchers to understand how their work fits into the broader academic conversation and contributes to the existing body of knowledge.

tl;dr  A literature review critically examines and synthesizes existing scholarly research and publications on a specific topic to provide a comprehensive understanding of the current state of knowledge in the field.

What is a literature review NOT?

❌ An annotated bibliography

❌ Original research

❌ A summary

❌ Something to be conducted at the end of your research

❌ An opinion piece

❌ A chronological compilation of studies

The reason for conducting a literature review is to:

Literature Reviews: An Overview for Graduate Students

While this 9-minute video from NCSU is geared toward graduate students, it is useful for anyone conducting a literature review.

Writing the literature review: A practical guide

Available 3rd floor of Perkins

Writing literature reviews: A guide for students of the social and behavioral sciences

Available online!

So, you have to write a literature review: A guided workbook for engineers

Telling a research story: Writing a literature review

The literature review: Six steps to success

Systematic approaches to a successful literature review

Request from Duke Medical Center Library

Doing a systematic review: A student's guide

• Next: Types of reviews >>
• Last Updated: Aug 20, 2024 3:37 PM
• URL: https://guides.library.duke.edu/litreviews

Services for...

• Faculty & Instructors
• Graduate Students
• Undergraduate Students
• International Students
• Patrons with Disabilities

• Harmful Language Statement
• Re-use & Attribution / Privacy
• Support the Libraries

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Review of river ice observation and data analysis technologies.

1. Introduction

1.1. background information, 1.2. importance of river ice information, 1.3. objective, 1.4. paper structure, 2. literature review methodology and inventory of observations.

• Committee on River Ice Processes and the Environment (CRIPE) (Workshop on the Hydraulics of Ice Covered Rivers) and
• The Symposium on Ice, under the umbrella of the Ice Research and Engineering Committee of International Association for Hydro-Environment Engineering and Research (IAHR).
• Table 1 provides a synopsis of various sensor and platform combinations for river ice observation. Different colors are used to indicate whether each combination has been documented or if it holds potential for future use in river ice observation.
• Table 2 , Table 3 and Table 4 include a column for quantitative or qualitative assessment, summarizing the comparative efficacy of different methods and techniques.
• Figure 1 (both top and bottom) displays AOIs for river ice presence, highlighting major rivers in the Northern Hemisphere.
• Figure 2 shows ice processes and represents ice information terms identified in the reviewed literature.
• Figure 3 outlines a methodological approach for extracting river ice information from observations.

3. River Ice Observation Technologies

3.1. sensor.

• Electro-optical and infrared (EO/IR) : EO/IR sensors, such as multispectral or hyperspectral cameras, acquire images of the surface in ultra-violet, visible, and infrared wavelengths. EO/IR imagery is valuable for monitoring and characterizing ice cover, dynamics, and conditions in clear weather. Thermal infrared images of river ice can be used to measure temperature, and estimate ice floe geometric and statistical characteristics, concentration, structure, and thickness. The resolution for EO/IR sensors spans from a few cm to about 1 km. Stereo imagery and motion videos can be used to extract digital surface models of river ice.
• Synthetic aperture radar (SAR) : SAR sensors operate in the microwave frequency range and can provide high-resolution images regardless of cloud cover or sunlight. SAR imagery acquired in X, C, L, and P bands in single (HH or VV), dual (HH-HV, HH-VV, or VV-VH), compact polarimetric, or Quad-Pol (HH-HV-VV) polarizations were used to extract river ice information. The resolution of SAR sensors ranges from sub-meter (e.g., 25 cm) to 100 m. Interferometric SAR (InSAR) technology can be used for change detection in ice cover [ 49 ] and for determining ice displacement [ 50 ].
• Microwave radiometers : microwave radiometers can measure brightness temperatures emitted by objects in frequencies of the microwave spectrum. They are useful for detecting the presence of ice on rivers and estimating ice thickness.
• Ice penetrating radar or ground penetrating radar (GPR) : GPR systems emit an electromagnetic signals down into the ice surface to measure ice thickness and detect internal ice structures, such as layers or fractures.
• Radar altimeters : radar altimeter measures the distance between the platform (aircraft or satellite) and the surface of the river.
• LIDAR (light detection and ranging) : LIDAR sensors emit laser pulses and measure the time it takes for the pulses to return after reflecting off a surface. Airborne, satellite, or terrestrial LIDAR can be used to map the topography of ice cover on rivers and measure water level or ice surface height in clear weather conditions.
• GNSS-IR : A global navigation satellite system-interferometric reflectometry (GNSS-IR) sensor exploits the interference between GNSS signals reflected off the observed surface and signals received directly from the GNSS satellite.
• Gravimetry : This sensor system measures changes in gravitational force to map gravitational fields.
• Acoustic profilers: Acoustic profilers are commonly used to measure water velocity and depth in rivers. They can also provide information on the presence of ice and its draft.
• Ice thickness gauges : these sensors/tools can utilize various principles (e.g., temperature or conductivity profiles) to measure the thickness of ice and snow covers.

3.2. Platforms

• Satellites : Satellites orbit the Earth at various (from 160 km and above) altitudes and can carry a range of sensors to observe rivers and provide geospatial data acquisition at various scales. While satellites require substantial initial investment, once launched, they can facilitate global data accessibility and provide near-real-time (NRT) access.
• Aerial : Airplanes, helicopters, and unmanned aerial vehicles (UAVs) (also known as unoccupied aerial systems (UAS), remotely piloted aircraft (RPA), or drones) are commonly used for river ice observation. They can carry a variety of sensors, including cameras, LIDAR, SAR, GPR, and hyperspectral imagers. Aircrafts are particularly useful for high-resolution imaging and targeted observations of specific areas of interest. UAVs are increasingly used for remote sensing tasks due to their versatility and relatively low cost. The cost efficiency of airborne observation compared to commercial SAR satellite data was highlighted in [ 9 ]. Air balloons could potentially be employed for river ice observation, but such examples were not found in the literature.
• In situ (also referred to as ground-based) : In situ sensors are instruments deployed on the ice, on the ground, underwater, or mounted on towers or other structures. In the case of river ice, ships (icebreakers) and vehicles moving on the ice surface can also be employed. In situ sensors are essential in collecting reliable ice information and are often used for the validation of remote sensing measurements collected from aerial or satellite platforms.

3.3. Satellites

3.3.1. eo/ir.

• Ground-based (in situ) observations: drill holes for measurements of ice thickness and snow depth, ice typing, GPR for ice thickness measurement, runoff observations at gauging stations, photographs and videos on ice cover extent, dynamics and ice jams;
• Aerial photos and videos of ice conditions and extent;
• Remote sensing data from other satellite sources (e.g., Landsat and Sentinel-2);
• Environmental data including air temperature and precipitation.

3.3.3. Other Satellites Technologies

• Microwave Radiometers
• Satellite Gravimetry

3.3.4. Multi-Sensor Observations

3.3.5. summary of satellites observations, 3.4. airborne instruments, 3.4.1. eo/ir cameras and scanners.

• their restriction to the visible and infrared portions of the electromagnetic spectrum to operate in low visibility conditions (e.g., clouds, fog),
• difficulties in ice characterization in the presence of snow cover, and
• challenges of aircraft navigation in poor meteorological conditions.
• surface roughness [ 46 ];
• topographic mapping of ice jams in the Mohawk River [ 117 , 118 ], including its extent, thickness estimation, severity, and evolution;
• broken anchor ice dams [ 118 ];
• shear wall height [ 119 ], identify and measure floe sizes, thickness and volume, and floe size distributions (which can be useful information for estimating loads on structures);
• ice jam, its state and elevations [ 120 ],
• ice thickness estimation at various stages of freeze-up on the river Sokna, Norway [ 121 ].

3.4.2. LIDAR

3.4.3. radars.

• Ice Penetrating Radar
• Microwave Radiometer

3.5. In Situ Observations

3.5.1. method implementations.

• Shore: including towers, bridges, or other structures which have elevation, and tramway,
• Ice surface: stationary or moving on ice surface such as sled or vehicle (e.g., snowmobile, car, air cushion vehicle (ACV) [ 138 ]),
• Ship [ 126 ],
• Underwater: submersibles or remotely operated vehicles (ROVs),
• Frozen in ice (gauges and buoys).

3.5.2. Observation Technologies

• Visual observations
• Photography and Video
• Thermal Sensors
• Gauges and buoys
• Ice Penetrating radar
• Imaging Radars
• Seismic Sensors

4. From Observations to River Ice Information

4.1. observation data processing and analysis, 4.2. ml technologies for river ice, 4.3. reporting and product dissemination.

• Generation of output products in mapping formats (compatible with Geographic Information Systems (GIS) software, such as ESRI shapefiles, GeoTIFFs, KML/KMZ files), and in data formats (such as CSV, JSON, Excel) for sharing and integration with other software tools, supporting statistical analysis and visualization.
• Database storage allowing for easy access, retrieval, and querying, facilitating further analytics and data processing.
• Web-based platforms or portals enabling users to access and interact with the data online, providing interactive maps, visualization tools, and download options.
• Application Programming Interface (API) endpoints, which enable programmatically accessing and integrating river ice data into custom applications or systems, enhancing accessibility and interoperability.
• Social media platforms and media coverage, which can provide real-time updates and raise awareness about river ice conditions.
• Mobile applications, which can provide convenient access to river ice information, including alerts, maps, and crowd-sourced observations.
• Email alerts and newsletters, which deliver updates on river ice conditions, forecasts, and advisories to subscribers.

4.4. River Ice Observations for Hydraulic Models

4.5. existing operational river ice monitoring services, 4.5.1. canada, 4.5.2. european products, 4.5.4. international projects, 5. discussion, 5.2. challenges, 5.3. future directions and opportunities, 6. conclusions, author contributions, data availability statement, conflicts of interest, appendix a. river ice terms representing the observed river ice information.

• Agglomerate ice [ 194 ]
• Agglomerated skim ice (ASI) is more packed than juxtaposed skim ice and therefore has higher SAR backscatter [ 90 ]
• Anchor ice [ 163 ]
• Aufeis is a deposit of ice on the surface of the ground or exposed structures, produced by the freezing of periodically flowing water [ 32 ]
• Black ice [ 70 ]
• Break-up (e.g., [ 52 , 59 ])
• Border ice [ 73 , 75 , 81 ]
• Brash ice [ 9 ]
• Broken ice [ 71 ]
• Candled ice [ 195 , 196 ]
• Columnar ice [ 81 , 82 , 197 ]
• Congestion is stagnation of the ice cover at choking locations [ 175 ]
• Consolidated ice [ 70 , 72 , 73 , 75 , 81 ]
• Clear ice is a smooth ice sheet, appears in gray tone in SAR image [ 71 ]
• Crack [ 71 ]
• Frazil ice in different forms (slush, clusters/flocks, pans/pancake, hanging dam) [ 32 , 195 , 196 ]
• Frazil floes [ 9 ]
• Frazil pans [ 72 , 75 ]
• Frazil run [ 90 ]
• Hanging dams are made of frazil ice transported under an existing surface ice cover and depositing under the ice surface in slow-flowing locations (e.g., [ 198 ])
• Ice bridging [ 81 ]
• Ice concentration [ 65 ]
• Ice decay is the changes in ice-covered areas with melt onset and start of break-up [ 83 ]
• Ice extent (spatial) is the area (in km 2 ) of river ice [ 62 ]
• Ice floes [ 81 ]
• Ice flow choking points are locations with 100% ice concentration at the water surface [ 175 ]
• Ice-free [ 53 ]
• Ice front is defined by two criteria: (i) the ice front is the boundary between partial and complete ice coverage; and (ii) the frazil pans and floes must be static [ 72 ]
• Ice heap : agglomeration of broken ice up to 10 m thick [ 131 ]
• Ice velocity is dividing the measured displacements to the time difference [ 57 ]
• Icing shell [ 5 ] forms horizontally, close to the water surface, and develop from waves that repeatedly flood cold surfaces (observed along the banks of turbulent channel segments such as rapids or riffles)
• Intact ice is ice with a relatively smooth surface [ 79 , 110 ]
• Intraseasonal cycle from ice onset to ice break-up and total melting [ 58 ] (i.e., duration of ice cover including time of ice clearing)
• Jam [ 79 ], including break-up jam and freeze-up jam [ 199 , 200 ]
• Juxtaposed ice is formed when ice floes gradually thicken and adhere to each other [ 70 ]; its rough ice–water interface and a coarse ice structure cause the medium to moderately strong SAR backscatter
• Mixed ice/water is the ice cover condition after beginning of break-up until open water. It was defined based on the reflectance (values 0.1–0.5) of MODIS Band 2 [ 45 ]
• Navigation track is an ice opening for ship navigation pathway, may contain brash ice [ 71 ]
• New ice is ice which was recently formed [ 71 ]
• Phenology is the duration of ice period and time of its appearance, accumulation, and disappearance for a certain river reach [ 64 ]
• Ridge [ 71 ]
• Rubble ice is resulted from mechanical break-ups and it has a rough top surface (texture) [ 95 ]
• Rough ice [ 71 ]
• Sail height serves as a measure of the surface roughness of the ice cover, determined from the thermal ice surface up the average tops of larger protruding ice pieces of ice by visually lining them up with the horizon [ 75 ]
• Shore ice [ 72 ] composed of frazil slush, melting ice, and brash [ 9 ], shore-fast frazil ice [ 73 ]
• Skim ice [ 3 , 4 , 84 ]
• Smooth ice cover is represented by consolidated frazil or columnar (thermal) ice [ 77 , 95 ]
• Snow ice , also known mainly as white ice [ 198 ], is always formed on an established ice cover from an overlying snow cover saturated with rain or river water infiltration that has refrozen subsequently (has small and granular crystals) [ 81 , 197 ]
• Spray ice is made by water splashing and freezing and it is generally observed close to waterfalls, cascades, or steps [ 5 ]
• Thermal ice is formed mainly near the shore, where water is slow moving. Thermal ice crystals are large and display a tubular form [ 70 ]
• Thickness of ice cover is the distance between the air–ice (or snow–ice) and ice–water interfaces [ 85 ]
• White ice [ 53 ].
• Brooks, R.N.; Prowse, T.D.; O’Connell, I.J. Quantifying Northern Hemisphere Freshwater Ice. Geophys. Res. Lett. 2013 , 40 , 1128–1131. [ Google Scholar ] [ CrossRef ]
• Environment Canada. MANICE: Manual of Standard Procedures for Observing and Reporting Ice Conditions , 9th ed.; Canadian Ice Service: Ottawa, ON, Canada, 2005; ISBN 978-0-660-62858-5.
• Shen, H.T. Mathematical Modeling of River Ice Processes. Cold Reg. Sci. Technol. 2010 , 62 , 3–13. [ Google Scholar ] [ CrossRef ]
• Shen, H.T. River Ice Processes. In Advances in Water Resources Management ; Wang, L.K., Yang, C.T., Wang, M.-H.S., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 483–530. ISBN 978-3-319-22923-2. [ Google Scholar ]
• Turcotte, B.; Morse, B. A Global River Ice Classification Model. J. Hydrol. 2013 , 507 , 134–148. [ Google Scholar ] [ CrossRef ]
• Beltaos, S. River Ice Jams ; Water Resources Publications: Highland Rancch, CO, USA, 1995; ISBN 978-0-918334-87-9. [ Google Scholar ]
• Nafziger, J.; She, Y.; Hicks, F. Dynamic River Ice Processes in a River Delta Network. Cold Reg. Sci. Technol. 2019 , 158 , 275–287. [ Google Scholar ] [ CrossRef ]
• Daly, S.F. International Association for Hydaulic Research Working Group on Thermal Regimes: Report on Frazil Ice ; International Association for Hydraulic Research Working Group on Thermal Regimes; Cold Regions Research and Engineering Laboratory (CRREL): Hanover, NH, USA, 1994. [ Google Scholar ]
• Weber, F.; Nixon, D.; Hurley, J. Identification of River Ice Types on the Peace River Using RADARSAT-1 SAR Imagery. In Proceedings of the 11th River Ice Workshop, Committee on River Ice Processes and the Environment (CGU-HS), Université d’Ottawa, Ottawa, ON, Canada, 14–16 May 2001. [ Google Scholar ]
• CCRS Tutorial: Fundamentals of Remote Sensing. 2016. Available online: https://natural-resources.canada.ca/sites/nrcan/files/earthsciences/pdf/resource/tutor/fundam/pdf/fundamentals_e.pdf (accessed on 5 April 2024).
• Daly, S.F. The Corps of Engineers. Ice Engineering Manual. J. Cold Reg. Eng. 2007 , 21 , 41–46. [ Google Scholar ] [ CrossRef ]
• Burrell, B.C.; Turcotte, B.; Comfort, G.; Groeneveld, J. Infrastructure and River Ice: An Overview. In Proceedings of the 21st Workshop on the Hydraulics of Ice, Saskatoon, SK, Canada, 29 August–1 September 2021. CGU HS Committee on River Ice Processes and the Environment. [ Google Scholar ]
• Hicks, F.E. An Introduction to River Ice Engineering for Civil Engineers and Geoscientists ; CreateSpace Independent Publishing Platform: Charleston, SC, USA, 2016. [ Google Scholar ]
• Beltaos, S.; Burrell, B.C. Climatic Change and River Ice Breakup. Can. J. Civ. Eng. 2003 , 30 , 145–155. [ Google Scholar ] [ CrossRef ]
• Magnuson, J.J.; Robertson, D.M.; Benson, B.J.; Wynne, R.H.; Livingstone, D.M.; Arai, T.; Assel, R.A.; Barry, R.G.; Card, V.; Kuusisto, E.; et al. Historical Trends in Lake and River Ice Cover in the Northern Hemisphere. Science 2000 , 289 , 1743–1746. [ Google Scholar ] [ CrossRef ]
• Prowse, T.D.; Beltaos, S. Climatic Control of River-Ice Hydrology: A Review. Hydrol. Process. 2002 , 16 , 805–822. [ Google Scholar ] [ CrossRef ]
• Prowse, T.D.; Bonsal, B.R. Historical Trends in River-Ice Break-up: A Review. Hydrol. Res. 2004 , 35 , 281–293. [ Google Scholar ] [ CrossRef ]
• Yang, X.; Pavelsky, T.M.; Allen, G.H. The Past and Future of Global River Ice. Nature 2020 , 577 , 69–73. [ Google Scholar ] [ CrossRef ]
• Rokaya, P. Impacts of Climate and Regulation on Ice-Jam Flooding of Northern Rivers and Their Inland Deltas. Ph.D. Thesis, University of Saskatchewan, Saskatoon, SK, Canada, 2018. [ Google Scholar ]
• Timalsina, N.P. Ice Conditions in Norwegian Rivers Regulated for Hydropower: An Assessment in the Current and Future Climate, Skipnes Kommunikasjon as: Skipnes Kommunikasjon. Ph.D. Thesis, Norwegian University of Science and Technology (NTNU), Trondheim, Norway, 2014. [ Google Scholar ]
• Government of Yukon. Yukon State of the Environment Report 2020. A Report on Environmental Indicators ; 2020. Available online: https://open.yukon.ca/sites/default/files/yukon-state-environment-report-2020.pdf (accessed on 5 April 2024).
• Brown, D.R.N.; Arp, C.D.; Brinkman, T.J.; Cellarius, B.A.; Engram, M.; Miller, M.E.; Spellman, K.V. Long-Term Change and Geospatial Patterns of River Ice Cover and Navigability in Southcentral Alaska Detected with Remote Sensing. Arct. Antarct. Alp. Res. 2023 , 55 , 2241279. [ Google Scholar ] [ CrossRef ]
• Beltaos, S.; Prowse, T.D. Climate Impacts on Extreme Ice-Jam Events in Canadian Rivers. Hydrol. Sci. J. 2001 , 46 , 157–181. [ Google Scholar ] [ CrossRef ]
• Ettema, R. Information Needs When Estimating Ice Jam Floods and Ice Runs. In Extreme Hydrological Events: New Concepts for Security ; Vasiliev, O.F., Van Gelder, P.H.A.J.M., Plate, E.J., Bolgov, M.V., Eds.; NATO Science Series; Springer: Dordrecht, The Netherlands, 2007; Volume 78, pp. 285–298. ISBN 978-1-4020-5739zxcv-7. [ Google Scholar ]
• Lindenschmidt, K.-E.; Alfredsen, K.; Carstensen, D.; Choryński, A.; Gustafsson, D.; Halicki, M.; Hentschel, B.; Karjalainen, N.; Kögel, M.; Kolerski, T.; et al. Assessing and Mitigating Ice-Jam Flood Hazards and Risks: A European Perspective. Water 2022 , 15 , 76. [ Google Scholar ] [ CrossRef ]
• Turcotte, B.; Morse, B.; Pelchat, G. Impact of Climate Change on the Frequency of Dynamic Breakup Events and on the Risk of Ice-Jam Floods in Quebec, Canada. Water 2020 , 12 , 2891. [ Google Scholar ] [ CrossRef ]
• Lindenschmidt, K.-E.; Rokaya, P. A Stochastic Hydraulic Modelling Approach to Determining the Probable Maximum Staging of Ice-Jam Floods. J. Environ Inform. 2019 , 34 , 45–54. [ Google Scholar ] [ CrossRef ]
• Zahmatkesh, Z.; Kumar Jha, S.; Coulibaly, P.; Stadnyk, T. An Overview of River Flood Forecasting Procedures in Canadian Watersheds. Can. Water Resour. J. Rev. Can. Des Ressour. Hydr. 2019 , 44 , 213–229. [ Google Scholar ] [ CrossRef ]
• Arnal, L.; Pietroniro, A.C.; Pomeroy, J.W.; Fortin, V.; Casson, D.R.; Stadnyk, T.A.; Rokaya, P.; Durnford, D.; Friesenhan, E.; Clark, M.P. Towards a Coherent Flood Forecasting Framework for Canada: Local to Global Implications. J. Flood Risk Manag. 2023 , e12895. [ Google Scholar ] [ CrossRef ]
• van der Sanden, J.J. River Ice Mapping and Monitoring Using SAR Satellites ; Natural Resources Canada: Ottawa, ON, Canada, 2012; ISBN 978-1-100-19034-1.
• Kovachis, N.; Burrell, B.C.; Huokuna, M.; Beltaos, S.; Turcotte, B.; Jasek, M. Ice-Jam Flood Delineation: Challenges and Research Needs. Can. Water Resour. J. Rev. Can. Des Ressour. Hydr. 2017 , 42 , 258–268. [ Google Scholar ] [ CrossRef ]
• WMO. International Glossary of Hydrology = Glossaire International d’hydrologie = Mezhdunarodnyĭ Gidrologicheskiĭ Slovarʹ = Glosario Hidrológico Internacional , 3rd ed.; United Nations Educational, Scientific and Cultural Organization: Paris, France; World Meteorological Organization: Geneva, Switzerland, 2013; ISBN 978-92-3-001154-3. [ Google Scholar ]
• IAHR Multilingual Ice Terminology. 1980. Available online: https://rivergages.mvr.usace.army.mil/WaterControl/Districts/MVP/Reports/ice/iahr_ice_terminology.html (accessed on 5 April 2024).
• Unesco-WMO-IAHS. The Role of Snow and Ice in Hydrology: Proceedings of the Banff Symposia, September 1972 ; Unesco, the World Meteorological Organization, the International Association of the Hydrological Sciences: Banff, AB, Canada, 1973; Volume 1 and 2. [ Google Scholar ]
• Page, D.F.; Ramseier, R.O. Application of Radar Techniques to Ice and Snow Studies. J. Glaciol. 1975 , 15 , 171–191. [ Google Scholar ] [ CrossRef ]
• Arcone, S.A.; Delaney, A.J. Airborne River-Ice Thickness Profiling with Helicopter-Borne UHF Short-Pulse Radar. J. Glaciol. 1987 , 33 , 330–340. [ Google Scholar ] [ CrossRef ]
• Arcone, S.A. Dielectric Constant and Layer-Thickness Interpretation of Helicopter-Borne Short-Pulse Radar Waveforms Reflected from Wet and Dry River-Ice Sheets. IEEE Trans. Geosci. Remote Sens. 1991 , 29 , 768–777. [ Google Scholar ] [ CrossRef ]
• Rencz, A.N.; Ryerson, R.A. (Eds.) Manual of Remote Sensing , 3rd ed.; American Society for Photogrammetry and Remote Sensing; J. Wiley: New York, NY, USA, 1998; ISBN 978-0-471-29406-1. [ Google Scholar ]
• Rees, G. Remote Sensing of Snow and Ice ; Taylor & Francis: Boca Raton, FL, USA, 2006; ISBN 978-0-415-29831-5. [ Google Scholar ]
• Leconte, R.; Daly, S.; Gauthier, Y.; Yankielun, N.; Bérubé, F.; Bernier, M. A Controlled Experiment to Retrieve Freshwater Ice Characteristics from an FM-CW Radar System. Cold Reg. Sci. Technol. 2009 , 55 , 212–220. [ Google Scholar ] [ CrossRef ]
• Kay, R.L.; White, K.D. River Ice Data Instrumentation. In CRREL Report 97-2 ; Hydrologie Engineering Branch, Engineering Division: Davis, CA, USA, 1997. [ Google Scholar ]
• U.S. Army Corps of Engineers. Engineering and Design: ICE ENGINEERING ; Department of the Army: Washington, DC, USA, 2002; p. 377. [ Google Scholar ]
• Jeffries, M.O.; Morris, K.; Kozlenko, N. Ice Characteristics and Processes, and Remote Sensing of Frozen Rivers and Lakes. In Geophysical Monograph Series ; Duguay, C.R., Pietroniro, A., Eds.; American Geophysical Union: Washington, DC, USA, 2013; pp. 63–90. ISBN 978-1-118-66642-5. [ Google Scholar ]
• Duguay, C.R.; Bernier, M.; Gauthier, Y.; Kouraev, A. Remote Sensing of Lake and River Ice. In Remote Sensing of the Cryosphere ; Tedesco, M., Ed.; Wiley: New York, NY, USA, 2015; pp. 273–306. ISBN 978-1-118-36885-5. [ Google Scholar ]
• Cooley, S.W.; Pavelsky, T.M. Spatial and Temporal Patterns in Arctic River Ice Breakup Revealed by Automated Ice Detection from MODIS Imagery. Remote Sens. Environ. 2016 , 175 , 310–322. [ Google Scholar ] [ CrossRef ]
• Palomaki, R.T.; Sproles, E.A. Quantifying the Effect of River Ice Surface Roughness on Sentinel-1 SAR Backscatter. Remote Sens. 2022 , 14 , 5644. [ Google Scholar ] [ CrossRef ]
• Stonevicius, E.; Uselis, G.; Grendaite, D. Ice Detection with Sentinel-1 SAR Backscatter Threshold in Long Sections of Temperate Climate Rivers. Remote Sens. 2022 , 14 , 1627. [ Google Scholar ] [ CrossRef ]
• Verfaillie, M.; Cho, E.; Dwyre, L.; Khan, I.; Wagner, C.; Jacobs, J.M.; Hunsaker, A. UAS Remote Sensing Applications to Abrupt Cold Region Hazards. Front. Remote Sens. 2023 , 4 , 1095275. [ Google Scholar ] [ CrossRef ]
• Van der Sanden, J.; Drouin, H.; Bian, Y. Repeat Pass InSAR Observations of River and Lake Ice Cover: A Preliminary Evaluation of Information Content. In Proceedings of the 17th Workshop on River Ice, Edmonton, AB, Canada, 21–24 July 2013; pp. 258–271. [ Google Scholar ]
• Chu, T.; Lindenschmidt, K.E. Determining River Ice Displacement Using the Differential Interferometry Synthetic Aperture Radar (D-InSAR) Technique. In AGU Fall Meeting Abstracts ; American Geophysical Union: Washington, DC, USA, 2016; Volume 2016, p. C33B-0827. [ Google Scholar ]
• Schneider, S.R. Monitoring River Ice Break-up from Space. Photogramm. Eng. Remote Sens. 1978 , 44 , 57–68. [ Google Scholar ]
• Dey, B.; Moore, H.; Gregory, A.F. The Use of Satellite Imagery for Monitoring Ice Break-up along the Mackenzie River, N. W. T. Arctic 1977 , 30 , 234–242. [ Google Scholar ] [ CrossRef ]
• Gatto, L.W. Monitoring River Ice with Landsat Images. Remote Sens. Environ. 1990 , 32 , 1–16. [ Google Scholar ] [ CrossRef ]
• Pavelsky, T.M.; Smith, L.C. Spatial and Temporal Patterns in Arctic River Ice Breakup Observed with MODIS and AVHRR Time Series. Remote Sens. Environ. 2004 , 93 , 328–338. [ Google Scholar ] [ CrossRef ]
• Liu, L.; Xu, Q.; Yang, S. Identification of River Ice on the Yellow River Using LANDSAT Images. In Proceedings of the 2010 18th International Conference on Geoinformatics, Beijing, China, 18–20 June 2010; IEEE: Piscataway, NJ, USA; pp. 1–4. [ Google Scholar ]
• Kääb, A.; Prowse, T. Cold-Regions River Flow Observed from Space: COLD-REGIONS RIVER FLOW FROM SPACE. Geophys. Res. Lett. 2011 , 38 , 1–5. [ Google Scholar ] [ CrossRef ]
• Kääb, A.; Lamare, M.; Abrams, M. River Ice Flux and Water Velocities along a 600 Km-Long Reach of Lena River, Siberia, from Satellite Stereo. Hydrol. Earth Syst. Sci. 2013 , 17 , 4671–4683. [ Google Scholar ] [ CrossRef ]
• Chaouch, N.; Temimi, M.; Romanov, P.; Cabrera, R.; McKillop, G.; Khanbilvardi, R. An Automated Algorithm for River Ice Monitoring over the Susquehanna River Using the MODIS Data. Hydrol. Process. 2014 , 28 , 62–73. [ Google Scholar ] [ CrossRef ]
• Muhammad, P.; Duguay, C.; Kang, K.-K. Monitoring Ice Break-up on the Mackenzie River Using MODIS Data. Cryosphere 2016 , 10 , 569–584. [ Google Scholar ] [ CrossRef ]
• Beaton, A.; Whaley, R.; Corston, K.; Kenny, F. Identifying Historic River Ice Breakup Timing Using MODIS and Google Earth Engine in Support of Operational Flood Monitoring in Northern Ontario. Remote Sens. Environ. 2019 , 224 , 352–364. [ Google Scholar ] [ CrossRef ]
• Kääb, A.; Altena, B.; Mascaro, J. River-Ice and Water Velocities Using the Planet Optical Cubesat Constellation. Hydrol. Earth Syst. Sci. 2019 , 23 , 4233–4247. [ Google Scholar ] [ CrossRef ]
• Li, H.; Li, H.; Wang, J.; Hao, X. Monitoring High-Altitude River Ice Distribution at the Basin Scale in the Northeastern Tibetan Plateau from a Landsat Time-Series Spanning 1999–2018. Remote Sens. Environ. 2020 , 247 , 111915. [ Google Scholar ] [ CrossRef ]
• Altena, B.; Kääb, A. Quantifying River Ice Movement through a Combination of European Satellite Monitoring Services. Int. J. Appl. Earth Obs. Geoinf. 2021 , 98 , 102315. [ Google Scholar ] [ CrossRef ]
• Li, H.; Li, H.; Wang, J.; Hao, X. Revealing the River Ice Phenology on the Tibetan Plateau Using Sentinel-2 and Landsat 8 Overlapping Orbit Imagery. J. Hydrol. 2023 , 619 , 129285. [ Google Scholar ] [ CrossRef ]
• Temimi, M.; Abdelkader, M.; Tounsi, A.; Chaouch, N.; Carter, S.; Sjoberg, B.; Macneil, A.; Bingham-Maas, N. An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery. Remote Sens. 2023 , 15 , 4896. [ Google Scholar ] [ CrossRef ]
• Beltaos, S.; Kääb, A. Estimating River Discharge during Ice Breakup from Near-Simultaneous Satellite Imagery. Cold Reg. Sci. Technol. 2014 , 98 , 35–46. [ Google Scholar ] [ CrossRef ]
• Pelletier, K.; Hicks, F.; van der Sanden, J. Monitoring Breakup on the Athabasca River Using RADARSAT-1 SAR Imagery. In Proceedings of the 12th Workshop on River Ice, Canadian Geophysical Union—Hydrology Section, Committee on River Ice Processes and the Environment. Edmonton, AB, Canada, 19–20 June 2003; pp. 69–94. [ Google Scholar ]
• Pelletier, K.; van der Sanden, J.; Hicks, F. Synthetic Aperture Radar: Current Capabilities and Limitations for River Ice Monitoring. In Proceedings of the 17th Canadian Hydrotechnical Conference, Edmonton, AB, Canada, 17–19 August 2005; pp. 1–10. [ Google Scholar ]
• Pelletier, K. Radar Remote Sensing of River Ice on the Athabasca River at Fort McMurray, Alberta. Master’s Thesis, University of Alberta, Edmonton, AB, Canada, 2006. [ Google Scholar ]
• Chu, T.; Das, A.; Lindenschmidt, K.-E. Monitoring the Variation in Ice-Cover Characteristics of the Slave River, Canada Using RADARSAT-2 Data—A Case Study. Remote Sens. 2015 , 7 , 13664–13691. [ Google Scholar ] [ CrossRef ]
• Gatto, L. River Ice Conditions Determined from ERS-1 SAR. In Proceedings of the 50th Eastern Snow Conference and the 61 st Western Snow Conference, Quebec City, QC, Canada, 8–10 June 1993; Volume 7. [ Google Scholar ]
• Weber, F.; Nixon, D.; Hurley, J. Semi-Automated Classification of River Ice Types on the Peace River Using RADARSAT-1 Synthetic Aperture Radar (SAR) Imagery. Can. J. Civ. Eng. 2003 , 30 , 11–27. [ Google Scholar ] [ CrossRef ]
• Jasek, M.; Weber, F.; Hurley, J. Ice Thickness and Roughness Analysis on the Peace River Using RADARSAT-1 SAR Imagery. In Proceedings of the 12th Workshop on the Hydraulics of Ice Covered Rivers, Edmonton, AB, Canada, 19–20 June 2003; pp. 19–20. [ Google Scholar ]
• Puestow, T.; Randell, C.; Rollings, K.; Khan, A.A.; Picco, R. Near Real-Time Monitoring of River Ice in Support of Flood Forecasting in Eastern Canada: Towards the Integration of Earth Observation Technology in Flood Hazard Mitigation. In Proceedings of the IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA, 20–24 September 2004; Volume 4, pp. 2268–2271. [ Google Scholar ]
• Gauthier, Y.; Weber, F.; Savary, S.; Jasek, M.; Paquet, L.-M.; Bernier, M. A Combined Classification Scheme to Characterise River Ice from SAR Data. A Comb. Classif. Scheme Characterise River Ice SAR Data 2006 , 5 , 77–88. [ Google Scholar ]
• Gherboudj, I.; Bernier, M.; Leconte, R. Validation of a Backscatter Model of a River Ice Covers Using Radarsat-1 Images. In Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 23–28 July 2007; pp. 1087–1090. [ Google Scholar ]
• Drouin, H.; Gauthier, Y.; Bernier, M.; Jasek, M.; Penner, O.; Weber, F. Quantitative Validation of RADARSAT-1 River Ice Maps. In Proceedings of the 14th Workshop of the Committee on River Ice Processes and the Environment, Quebec City, QC, Canada, 19–22 June 2007. [ Google Scholar ]
• Mermoz, S.; Allain, S.; Bernier, M.; Pottier, E. Investigation of Radarsat-2 and Terrasar-X Data for River Ice Classification. In Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa, 12–17 July 2009; pp. II-29–II-32. [ Google Scholar ]
• Unterschultz, K.D.; van der Sanden, J.; Hicks, F. Potential of RADARSAT-1 for the Monitoring of River Ice: Results of a Case Study on the Athabasca River at Fort McMurray, Canada. Cold Reg. Sci. Technol. 2009 , 55 , 238–248. [ Google Scholar ] [ CrossRef ]
• Lindenschmidt, K.-E.; Syrenne, G.; Harrison, R. Measuring Ice Thicknesses along the Red River in Canada Using RADARSAT-2 Satellite Imagery. JWARP 2010 , 2 , 923–933. [ Google Scholar ] [ CrossRef ]
• Lindenschmidt, K.-E.; van der Sanden, J.; Demski, A.; Drouin, H.; Geldsetzer, T. Characterising River Ice along the Lower Red River Using RADARSAT-2 Imagery. In Proceedings of the 16th Workshop on River Ice, Winnipeg, MB, Canada, 18–22 September 2011; pp. 18–22. [ Google Scholar ]
• Van der Sanden, J.; Drouin, H. Satellite SAR Observations of River Ice Cover: A RADARSAT-2 (C-Band) and ALOS PALSAR (L-Band) Comparison. In Proceedings of the 16th Workshop on River Ice, Winnipeg, MB, Canada, 18–22 September 2011; pp. 18–22. [ Google Scholar ]
• Sobiech, J.; Dierking, W. Observing Lake- and River-Ice Decay with SAR: Advantages and Limitations of the Unsupervised k -Means Classification Approach. Ann. Glaciol. 2013 , 54 , 65–72. [ Google Scholar ] [ CrossRef ]
• Jasek, M.; Gauthier, Y.; Poulin, J.; Bernier, M. Monitoring of Freeze-up on the Peace River at the Vermilion Rapids Using RADARSAT-2 SAR Data. In Proceedings of the 17th Workshop on River Ice, Edmonton, AB, Canada, 21–24 July 2013. [ Google Scholar ]
• Mermoz, S.; Allain, S.; Bernier, M.; Pottier, E.; Van Der Sanden, J.; Chokmani, K. Retrieval of River Ice Thickness from C-Band PolSAR Data. In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012; pp. 3237–3240. [ Google Scholar ]
• Mermoz, S.; Allain-Bailhache, S.; Bernier, M.; Pottier, E.; Van Der Sanden, J.; Chokmani, K. Retrieval of River Ice Thickness from C-Band PolSAR Data. IEEE Trans. Geosci. Remote Sens. 2014 , 52 , 3052–3062. [ Google Scholar ] [ CrossRef ]
• Floyd, A.L.; Prakash, A.; Meyer, F.J.; Gens, R.; Liljedahl, A. Using Synthetic Aperture Radar to Define Spring Breakup on the Kuparuk River, Northern Alaska. ARCTIC 2014 , 67 , 462. [ Google Scholar ] [ CrossRef ]
• Watanabe, M.; Takakura, H.; Yonezawa, C.; Yoshikawa, Y.; Shimada, M. Estimation of Cause of Ice Jam Flooding in Sub-Arctic Regions Using PALSAR Full Polarimetry Data. In Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 13–18 July 2014; pp. 843–846. [ Google Scholar ]
• Das, A.; Sagin, J.; Van Der Sanden, J.; Evans, E.; McKay, H.; Lindenschmidt, K.-E. Monitoring the Freeze-up and Ice Cover Progression of the Slave River. Can. J. Civ. Eng. 2015 , 42 , 609–621. [ Google Scholar ] [ CrossRef ]
• Łoś, H.; Osińska-Skotak, K.; Pluto-Kossakowska, J.; Bernier, M.; Gauthier, Y.; Jasek, M.; Roth, A. Comparison of C-Band and X-Band Polarimetric Sar Data for River Ice Classification on the Peace River. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2016 , XLI-B7 , 543–548. [ Google Scholar ] [ CrossRef ]
• Los, H.; Pawlowski, B. The Use of Sentinel-1 Imagery in the Analysis of River Ice Phenomena on the Lower Vistula in the 2015–2016 Winter Season. In Proceedings of the 2017 Signal Processing Symposium (SPSympo), Jachranka Village, Poland, 12–14 September 2017; pp. 1–5. [ Google Scholar ]
• Lindenschmidt, K.-E.; Li, Z. Monitoring River Ice Cover Development Using the Freeman–Durden Decomposition of Quad-Pol Radarsat-2 Images. J. Appl. Rem. Sens. 2018 , 12 , 026014. [ Google Scholar ] [ CrossRef ]
• Huang, P.; Shi, Q.; Tan, W.; Xu, W. Yellow River Ice Decision Tree Classification Method Based on Polarimetric Sar Data. In Proceedings of the IGARSS 2019—2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019; pp. 4129–4132. [ Google Scholar ]
• Zhang, F.; Li, Z.; Lindenschmidt, K.-E. Potential of RADARSAT-2 to Improve Ice Thickness Calculations in Remote, Poorly Accessible Areas: A Case Study on the Slave River, Canada. Can. J. Remote Sens. 2019 , 45 , 234–245. [ Google Scholar ] [ CrossRef ]
• van der Sanden, J.J.; Drouin, H.; Geldsetzer, T. An Automated Procedure to Map Breaking River Ice with C-Band HH SAR Data. Remote Sens. Environ. 2021 , 252 , 112119. [ Google Scholar ] [ CrossRef ]
• De Roda Husman, S.; Van Der Sanden, J.J.; Lhermitte, S.; Eleveld, M.A. Integrating Intensity and Context for Improved Supervised River Ice Classification from Dual-Pol Sentinel-1 SAR Data. Int. J. Appl. Earth Obs. Geoinf. 2021 , 101 , 102359. [ Google Scholar ] [ CrossRef ]
• Zhang, H.; Li, H.; Li, H. Monitoring the Ice Thickness in High-Order Rivers on the Tibetan Plateau with Dual-Polarized C-Band Synthetic Aperture Radar. Remote Sens. 2022 , 14 , 2591. [ Google Scholar ] [ CrossRef ]
• Marthandavilakom Prakasam, G. Detecting Ice Jams on the Rivers in Northern Finland Using Sentinel-1. Master’s Thesis, Aalto University, Espoo, Finland, 2022. [ Google Scholar ]
• Liu, B.; Ji, H.; Zhai, Y.; Luo, H. Estimation of River Ice Thickness in the Shisifenzi Reach of the Yellow River With Remote Sensing and Air Temperature Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023 , 16 , 5645–5659. [ Google Scholar ] [ CrossRef ]
• Kouraev, A.V.; Zakharova, E.A.; Samain, O.; Mognard, N.M.; Cazenave, A. Ob’ River Discharge from TOPEX/Poseidon Satellite Altimetry (1992–2002). Remote Sens. Environ. 2004 , 93 , 238–245. [ Google Scholar ] [ CrossRef ]
• Michailovsky, C.I.; McEnnis, S.; Berry, P.A.M.; Smith, R.; Bauer-Gottwein, P. River Monitoring from Satellite Radar Altimetry in the Zambezi River Basin. Hydrol. Earth Syst. Sci. 2012 , 16 , 2181–2192. [ Google Scholar ] [ CrossRef ]
• Bogning, S.; Frappart, F.; Blarel, F.; Niño, F.; Mahé, G.; Bricquet, J.-P.; Seyler, F.; Onguéné, R.; Etamé, J.; Paiz, M.-C.; et al. Monitoring Water Levels and Discharges Using Radar Altimetry in an Ungauged River Basin: The Case of the Ogooué. Remote Sens. 2018 , 10 , 350. [ Google Scholar ] [ CrossRef ]
• Zakharova, E.; Nielsen, K.; Kamenev, G.; Kouraev, A. River Discharge Estimation from Radar Altimetry: Assessment of Satellite Performance, River Scales and Methods. J. Hydrol. 2020 , 583 , 124561. [ Google Scholar ] [ CrossRef ]
• Bjerklie, D.M.; Birkett, C.M.; Jones, J.W.; Carabajal, C.; Rover, J.A.; Fulton, J.W.; Garambois, P.-A. Satellite Remote Sensing Estimation of River Discharge: Application to the Yukon River Alaska. J. Hydrol. 2018 , 561 , 1000–1018. [ Google Scholar ] [ CrossRef ]
• Guo, X.; Jin, S.; Zhang, Z. Evaluation of Water Level Estimation in the Upper Yangtze River from ICESat-2 Data. In Proceedings of the 2021 Photonics & Electromagnetics Research Symposium (PIERS), Hangzhou, China, 21–25 November 2021; pp. 2260–2264. [ Google Scholar ]
• Podkowa, A.; Kugler, Z.; Nghiem, S.V.; Brakenridge, G.R. Ice Freeze-Up and Break-Up in Arctic Rivers Observed With Satellite L-Band Passive Microwave Data From 2010 to 2020. Water Resour. Res. 2023 , 59 , e2022WR031939. [ Google Scholar ] [ CrossRef ]
• Wang, S.; Zhou, F.; Russell, H. Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations. Remote Sens. 2017 , 9 , 256. [ Google Scholar ] [ CrossRef ]
• Rybushkina, G.; Troitskaya, Y.; Soustova, I. Ice Cover Determination of the Volga and the Don River Reservoirs on the Base of Jason-2 Sattelite Observations. In Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 13–18 July 2014; pp. 149–152. [ Google Scholar ]
• Zakharova, E.; Agafonova, S.; Duguay, C.; Frolova, N.; Kouraev, A. River ice phenology and thickness from satellite altimetry: Potential for ice bridge road operation and climate studies. Cryosphere 2021 , 15 , 5387–5407. [ Google Scholar ] [ CrossRef ]
• Chu, T.; Lindenschmidt, K.-E. Integration of Space-Borne and Air-Borne Data in Monitoring River Ice Processes in the Slave River, Canada. Remote Sens. Environ. 2016 , 181 , 65–81. [ Google Scholar ] [ CrossRef ]
• Lindenschmidt, K.-E.; Chun, K.P. Geospatial Modelling to Determine the Behaviour of Ice Cover Formation during Freeze-up of the Dauphin River in Manitoba. Hydrol. Res. 2014 , 45 , 645–659. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Yue, Y.; Han, L.; Li, F.; Yuan, X.; Fan, M.; Zhang, Y. River Ice Monitoring and Change Detection with Multi-Spectral and SAR Images: Application over Yellow River. Multimed. Tools Appl. 2021 , 80 , 28989–29004. [ Google Scholar ] [ CrossRef ]
• Amani, M.; Mahdavi, S.; Jin, S. River Ice Monitoring Using Unsupervised ISODATA Algorithm and Different Optical and SAR Satellite Datasets: A Case Study from the Churchill River in Labrador, Canada. J. Ocean. Technol. 2024 , 18 , 99. [ Google Scholar ]
• Alberta Environment. Peace River Ice Observation Report ; Alberta Environment: Edmonton, AB, Canada, 2007. [ Google Scholar ]
• Morse, B. Winter Navigation on the St. Lawrence River. In Proceedings of the International Conference on Port and Ocean Engineering under Arctic Conditions, Ottawa, Canada, 12–17 August 2001. [ Google Scholar ]
• Lin, J.; Shu, L.; Zuo, H.; Zhang, B. Experimental Observation and Assessment of Ice Conditions with a Fixed-Wing Unmanned Aerial Vehicle over Yellow River, China. J. Appl. Remote Sens 2012 , 6 , 063586. [ Google Scholar ] [ CrossRef ]
• Garver, J.I.; Capovani, E.; Pokrzywka, D. Photogrammetric Models from UAS Mapping and Ice Thickness Estimates of the 2018 Mid-Winter Ice Jam on the Mohawk River, NY. Cockburn JMH Garver JI Proc. 2018 , 10 , 19–24. [ Google Scholar ]
• Alfredsen, K.; Haas, C.; Tuhtan, J.A.; Zinke, P. Brief Communication: Mapping River Ice Using Drones and Structure from Motion. Cryosphere 2018 , 12 , 627–633. [ Google Scholar ] [ CrossRef ]
• Alfredsen, K.; Juarez, A. Modelling Stranded River Ice Using LiDAR and Drone-Based Models. In Proceedings of the 25th International Symposium, The International Association for Hydro-Environment Engineering and Research. Ice Trondheim, Norway, 23–25 November 2020. [ Google Scholar ]
• Duguay, J.; Lindenschmidt, K.-E.; Trudel, M.; Pruneau, A. Aerial Photogrammetry to Characterise and Numerically Model an Ice Jam in Southern Quebec. Hydrol. Res. 2023 , 54 , 1329–1343. [ Google Scholar ] [ CrossRef ]
• Rødtang, E.; Alfredsen, K.; Juárez, A. Drone Surveying of Volumetric Ice Growth in a Steep River. Front. Remote Sens. 2021 , 2 , 767073. [ Google Scholar ] [ CrossRef ]
• Emond, J.; Morse, B.; Richard, M.; Stander, E.; Viau, A.A. Surface Ice Observations on the St. Lawrence River Using Infrared Thermography. River Res. Apps 2011 , 27 , 1090–1105. [ Google Scholar ] [ CrossRef ]
• Dugdale, S.J.; Kelleher, C.A.; Malcolm, I.A.; Caldwell, S.; Hannah, D.M. Assessing the Potential of Drone-based Thermal Infrared Imagery for Quantifying River Temperature Heterogeneity. Hydrol. Process. 2019 , 33 , 1152–1163. [ Google Scholar ] [ CrossRef ]
• Ren, H.C.; Yan, Q.; Liu, Z.J.; Zuo, Z.Q.; Xu, Q.Q.; Li, F.F.; Song, C. Study on Analysis from Sources of Error for Airborne LIDAR. IOP Conf. Ser. Earth Environ. Sci. 2016 , 46 , 012030. [ Google Scholar ] [ CrossRef ]
• Kucharczyk, M.; Hugenholtz, C.H.; Zou, X. UAV–LiDAR Accuracy in Vegetated Terrain. J. Unmanned Veh. Sys. 2018 , 6 , 212–234. [ Google Scholar ] [ CrossRef ]
• Dean, A.M. Remote Sensing of Accumulated Frazil and Brash Ice in the St. Lawrence River ; Department of Defense, Department of the Army, Corps of Engineers, Cold: Hanover, NH, USA, 1977. [ Google Scholar ]
• Finkel’shteyn, M.; Lazarev, Ė.I. A Radar Video Pulse Device for Measuring the Thickness of Sea Ice as a New Promising Means of Ice Reconnaissance ; Cold Regions Research and Engineering Laboratory: Hanover, NH, USA, 1981. [ Google Scholar ]
• Yankielun, N.E.; Arcone, S.A.; Crane, R.K. Thickness Profiling of Freshwater Ice Using a Millimeter-Wave FM-CW Radar. IEEE Trans. Geosci. Remote Sens. 1992 , 30 , 1094–1100. [ Google Scholar ] [ CrossRef ]
• Yankielun, N.E.; Ferrick, M.G.; Weyrick, P.B. Development of an Airborne Millimeter-Wave FM-CW Radar for Mapping River Ice. Can. J. Civ. Eng. 1993 , 20 , 1057–1064. [ Google Scholar ] [ CrossRef ]
• Peshkov, A.N.; Finkelstein, M.I.; Smutov, A.I.; Vasin, A.G.; Klemiato, K.I. Using of Radiolocation Measuring Devices of Freshmater Ice Thickness. In Proceedings of the IGARSS’91 Remote Sensing: Global Monitoring for Earth Management, Espoo, Finland, 3–6 June 1991; Volume 3, p. 1575. [ Google Scholar ]
• Fedorov, M.P.; Fedorova, L.L.; Savvin, D.V.; Prudetskaya, T.P.; Omelyanenko, A.V. Investigation of Ice Jams Using the GPR Method. In Proceedings of the 2018 17th International Conference on Ground Penetrating Radar (GPR), Rapperswil, Switzerland, 18–21 June 2018; pp. 1–4. [ Google Scholar ]
• Bai, X.; Wang, L.; Luo, X.; Mi, H.; Chen, H.; Liu, L.; Ji, M.; Gao, Y. A Layer Tracking Method for Ice Thickness Detection Based on GPR Mounted on the UAV. In Proceedings of the 2020 4th International Conference on Imaging, Signal Processing and Communications (ICISPC), Kumamoto, Japan, 23–25 October 2020; pp. 24–28. [ Google Scholar ]
• Leconte, R.; Klassen, P.D. Lake and River Ice Investigations in Northern Manitoba Using Airborne SAR Imagery. ARCTIC 1991 , 44 , 153–163. [ Google Scholar ] [ CrossRef ]
• Mermoz, S.; Allain, S.; Bernier, M.; Pottier, E.; Gherboudj, I. Classification of River Ice Using Polarimetric SAR Data. Can. J. Remote Sens. 2009 , 35 , 460–473. [ Google Scholar ] [ CrossRef ]
• Catoe, C.; Ling, G.; Nordberg, W.; Thaddeus, P. Preliminary Results from Aircraft Flight Tests of an Electrically Scanning Microwave Radiometer ; NASA, Goddard Space Flight Center: Greenbelt, MD, USA, 1967.
• Melloh, R.A.; Gatto, L.W. Interpretation of Passive and Active Microwave Imagery over Snow-Covered Lakes and Rivers near Fairbanks Alaska. In Proceedings of the Application Remote Sensing Hydrology Workshop, Saskatoon, SK, Canada, 13–14 February 1990; pp. 259–278. [ Google Scholar ]
• Melloh, R.A.; Eppler, D.T.; Farmer, L.D.; Gatto, L.W.; Chacho, E.F. Interpretation of Passive Microwave Imagery of Surface Snow and Ice: Harding Lake, Alaska ; U.S. Army Corps of Engineers, Cold Regions Research & Engineering Laboratory: Hanover, NH, USA, 1991. [ Google Scholar ]
• Venier, G.O.; Cross, F.R.; Ramseier, R.O. Experiments with Mobile X-Band FM Radar in Measuring the Thi Ckness of Fresh-Water Ice ; CRC, Department of Communications: Ottawa, ON, Canada, 1975. [ Google Scholar ]
• de Koven Leffingwell, E. The Canning River Region, Northern Alaska ; Proffesional Paper 109; US Government Printing Office, USGS: Washington, DC, USA, 1919.
• Rossiter, J.R.; Crissman, R.D. Assessment of Instrumentation for Application to Winter Hydropower Operations on the Upper Niagara River. In Proceedings of the 12th IAHR International Symposium on Ice (Trondheim 1994), Trondheim, Norway, 23 August 1994; pp. 744–751. [ Google Scholar ]
• Ansari, S.; Rennie, C.D.; Seidou, O.; Malenchak, J.; Zare, S.G. Automated Monitoring of River Ice Processes Using Shore-Based Imagery. Cold Reg. Sci. Technol. 2017 , 142 , 1–16. [ Google Scholar ] [ CrossRef ]
• Bharathi, P.; Subashini, P. Texture Feature Extraction of Infrared River Ice Images Using Second-Order Spatial Statistics. World Acad. Sci. Eng. Technol. 2013 , 74 , 747–757. [ Google Scholar ]
• Beltaos, S.; Carter, T.; Rowsell, R. Measurements and Analysis of Ice Breakup and Jamming Characteristics in the Mackenzie Delta, Canada. Cold Reg. Sci. Technol. 2012 , 82 , 110–123. [ Google Scholar ] [ CrossRef ]
• Zufelt, J. Ice Motion Detector System. In Ice Engineering ; U.S. Army Cold Regions Research and Engineering Laboratory: Hanover, NH, USA, 1993. [ Google Scholar ]
• Arnborg, L.; Peippo, J.; Larsson, R. The Ice Gauge. An Instrument for Measuring Vertical Movements of Ice Surfaces. Geogr. Annaler. Ser. A Phys. Geogr. 1965 , 47 , 237. [ Google Scholar ] [ CrossRef ]
• Sherstone, D.A.; Prowse, T.D.; Gross, H. Development and Use of’ Hot-Wire’ and Conductivity Type Ice Measurement Gauges For Determination of Ice Thickness in Arctic Rivers. In Proceedings of the Symposium: Cold Regions Hydrology ; American Water Resources Association, Bethesda Maryland; University of Alaska-Fairbanks: Fairbanks, AK, USA, 1986; pp. 121–129. [ Google Scholar ]
• Du, C.; Wang, Q.; Liu, X.; Zhao, Y.; Deng, X.; Cui, L. Research and Application of Ice Thickness and Snow Depth Automatic Monitoring System. IEEE Trans. Instrum. Meas. 2017 , 66 , 325–331. [ Google Scholar ] [ CrossRef ]
• Cui, L.; Qin, J.; Deng, X. Freshwater Ice Thickness Apparatus Based on Differences in Electrical Resistance and Temperature. Cold Reg. Sci. Technol. 2015 , 119 , 37–46. [ Google Scholar ] [ CrossRef ]
• Ackley, S.; Dellile, B.; Tison, J.; Carnat, G.; Weissling, B.; Lewis, M.; Fristen, C.; Stammerjohn, S. Documentation for Sea Ice Mass Balance in the Antarctic (SIMBA). 2016. Available online: https://openpolar.no/Record/ftdatacite:10.7265%2Fn53f4mj7 (accessed on 5 April 2024).
• Rafat, A.; Kheyrollah Pour, H.; Spence, C.; Palmer, M.J.; MacLean, A. An Analysis of Ice Growth and Temperature Dynamics in Two Canadian Subarctic Lakes. Cold Reg. Sci. Technol. 2023 , 210 , 103808. [ Google Scholar ] [ CrossRef ]
• Lynch, M.; English, J.; Briggs, R. Churchill River Ice Thickness Monitoring–SIMBA ; Report R-20-005-1572; C-CORE: St. John’s, NL, Canada, 2020. [ Google Scholar ]
• Chave, R.A.J.; Lemon, D.D.; Fissel, D.B.; Dupuis, L.; Dumont, S. Real-Time Measurements of Ice Draft and Velocity in the St. Lawrence River. In Proceedings of the Oceans ’04 MTS/IEEE Techno-Ocean ’04 (IEEE Cat. No.04CH37600), Kobe, Japan, 9–12 November 2004; Volume 3, pp. 1629–1633. [ Google Scholar ]
• Morse, B.; Hessami, M.; Bourel, C. Characteristics of Ice in the St. Lawrence River. Can. J. Civ. Eng. 2003 , 30 , 766–774. [ Google Scholar ] [ CrossRef ]
• Mudge, T.; Sloat, J.; Chen, J. Discharge and Current Profiles under the Fee. In Proceedings of the IEEE/OES Eighth Working Conference on Current Measurement Technology, Southampton, UK, 28–29 June 2005; pp. 101–105. [ Google Scholar ]
• Wang, Y.; Tang, Y.; Zhang, Z. Experiment of Acoustic Tomography under Ice in Songhua River Basin. In Proceedings of the 2022 8th International Conference on Hydraulic and Civil Engineering: Deep Space Intelligent Development and Utilization Forum (ICHCE), Xi’an, China, 25–27 November 2022; pp. 431–438. [ Google Scholar ]
• Ismail, S.; Davis, J. Ice Jam Thickness Profiling on the Saint John River, New Brunswick. In Proceedings of the 11th International Association for Hydraulic Research Symposium on Ice, Banff, AB, Canada, 11–14 August 1992; pp. 383–394. [ Google Scholar ]
• Kämäri, M.; Alho, P.; Colpaert, A.; Lotsari, E. Spatial Variation of River-Ice Thickness in a Meandering River. Cold Reg. Sci. Technol. 2017 , 137 , 17–29. [ Google Scholar ] [ CrossRef ]
• Crissman, R.D.; Lalumiere, L.A. Radar Monitoring of Ice on the Upper Niagara River. In Proceedings of the 6th Cold Regions Engineering Specialty Conference, CRREL, Hanover, NH, USA, 26–28 February 1991; American Society of Civil Engineers (ASCE). pp. 406–415. [ Google Scholar ]
• Ursica, S. Identifying Subarctic River Thermal and Mechanical Ice Break-Up Using Seismic Sensing. Master’s Thesis, Umeå Universitet, Umeå, Sweden, 2021. [ Google Scholar ]
• Ghiasi, Y.; Duguay, C.R.; Murfitt, J.; van der Sanden, J.J.; Thompson, A.; Drouin, H.; Prévost, C. Application of GNSS Interferometric Reflectometry for the Estimation of Lake Ice Thickness. Remote Sens. 2020 , 12 , 2721. [ Google Scholar ] [ CrossRef ]
• Purnell, D.; Dabboor, M.; Matte, P.; Peters, D.; Anctil, F.; Ghobrial, T.; Pierre, A. Observations of River Ice Breakup Using GNSS-IR, SAR, and Machine Learning. IEEE Trans. Geosci. Remote Sens. 2024 , 62 , 5800613. [ Google Scholar ] [ CrossRef ]
• Pei, C.; She, Y.; Loewen, M. Deep Learning Based River Surface Ice Quantification Using a Distant and Oblique-Viewed Public Camera. Cold Reg. Sci. Technol. 2023 , 206 , 103736. [ Google Scholar ] [ CrossRef ]
• Singh, A.; Kalke, H.; Loewen, M.; Ray, N. Alberta River Ice Segmentation Dataset ; IEEE: Piscataway, NJ, USA, 2019. [ Google Scholar ] [ CrossRef ]
• Singh, A.; Kalke, H.; Loewen, M.; Ray, N. River Ice Segmentation with Deep Learning. IEEE Trans. Geosci. Remote Sens. 2020 , 58 , 7570–7579. [ Google Scholar ] [ CrossRef ]
• Sola, D.; Scott, K.A. Efficient Shallow Network for River Ice Segmentation. Remote Sens. 2022 , 14 , 2378. [ Google Scholar ] [ CrossRef ]
• Yang, Z.; Zhu, Y.; Zeng, X.; Zong, J.; Liu, X.; Tao, R.; Cong, X.; Yu, Y. An Easy Zero-Shot Learning Combination: Texture Sensitive Semantic Segmentation IceHrNet and Advanced Style Transfer Learning Strategy. arXiv 2023 , arXiv:2310.00310. [ Google Scholar ]
• Zhang, X.; Jin, J.; Lan, Z.; Li, C.; Fan, M.; Wang, Y.; Yu, X.; Zhang, Y. ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features. Remote Sens. 2020 , 12 , 221. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Zhou, Y.; Jin, J.; Wang, Y.; Fan, M.; Wang, N.; Zhang, Y. ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images. Remote Sens. 2021 , 13 , 633. [ Google Scholar ] [ CrossRef ]
• Reuß, F.; Wessel, B.; Roth, A. TerraSAR-X Ice/Non-Ice Mapping of the Mackenzie River Using a Convolution-al Neural Network. In Proceedings of the TerraSAR-X/TanDEM-X Science Team Meeting 2019, Oberpfaffenhofen, Germany, 21–24 October 2019. [ Google Scholar ] [ CrossRef ]
• Yang, Z.; Zong, J.; Zhu, Y.; Liu, X.; Tao, R.; Yu, Y. River Ice Regime Recognition Based on Deep Learning: Ice Concentration, Area, and Velocity. Water 2023 , 16 , 58. [ Google Scholar ] [ CrossRef ]
• Sun, X.; Zhang, X.; Huang, W.; Han, Z.; Lyu, X.; Ren, P. Sea Ice Classification Using Mutually Guided Contexts. IEEE Trans. Geosci. Remote Sens. 2023 , 61 , 4204019. [ Google Scholar ] [ CrossRef ]
• Barrette, P.D.; Khan, A.A.; Lindenschmidt, K.-E. A Glimpse at Twenty-Five Hydraulic Models for River Ice. In Proceedings of the 27th IAHR International Symposium on Ice, Gdańsk, Poland, 9 June 2024. [ Google Scholar ]
• Hicks, F.; Andrishak, R.; She, Y. Modeling Thermal and Dynamic River Ice Processes. In Proceedings of the 13th International Conference on Cold Regions Engineering, Orono, ME, USA, 23–26 July 2006; pp. 1–11. [ Google Scholar ]
• Zhang, F.; Mosaffa, M.; Chu, T.; Lindenschmidt, K.-E. Using Remote Sensing Data to Parameterize Ice Jam Modeling for a Northern Inland Delta. Water 2017 , 9 , 306. [ Google Scholar ] [ CrossRef ]
• Turcotte, Benoit Will There Be an Ice Bridge This Winter? Predicting Spatio-Temporal Freeze-up Patterns Along the Yukon River, Canada. In Proceedings of the 25th IAHR International Symposium on Ice, Trondheim, Norway, 24 November 2020.
• Gauthier, Y.; Paquet, L.-M.; Gonzalez, A.; Hicks, F.; Andrishak, R.; Bernier, M. Using the FRAZIL System in Support of Winter River Flow Modelling. In Proceedings of the 14th Workshop on the Hydraulics of Ice Covered Rivers, Quebec City, QC, Canada, 19 June 2007. [ Google Scholar ]
• Gauthier, Y.; Tremblay, M.; Bernier, M.; Furgal, C. Adaptation of a Radar-Based River Ice Mapping Technology to the Nunavik Context. Can. J. Remote Sens. 2010 , 36 , S168–S185. [ Google Scholar ] [ CrossRef ]
• Pryse-Phillips, A.; Woolgar, R.; Rogers, K.; Lynch, M.; Puestow, T.; Randell, C. Ice Observations on the Churchill River Using Satellite Imagery. In Proceedings of the 15th Workshop on River Ice, Canadian Geophysical Union—Hydrology Section, Committee on River Ice Processes and the Environment. St. John’s, NL, Canada, 15 June 2009. [ Google Scholar ]
• Russell, K.; Warren, S.; Howell, C.; Puestow, T.; Randell, C.; Khan, A.A.; Mahabir, C.; Tang, P.; Burakov, D.; Novik, N. Improved Satellite-Based River Ice Monitoring Using Dual-Polarized SAR Imagery. CGU HS Committee on River Ice Processes and the Environment. In Proceedings of the 15th Workshop on River Ice, Canadian Geophysical Union—Hydrology Section, Committee on River Ice Processes and the Environment. St. John’s, NL, Canada, 15 June 2009. [ Google Scholar ]
• Warren, S.; Puestow, T.; Richard, M.; Khan, A.A.; Mohammad, K.; Lindenschmidt, K.-E. Near Real-Time Ice-Related Flood Hazard Assessment on the Exploits River, Newfoundland. In Proceedings of the CGU HS Committee on River Ice Processes and the Environment (CRIPE), Whitehorse, Yukon, 9–12 July 2017. [ Google Scholar ]
• Puestow, T.; Cuff, A.; Richard, M.; Van Der Sanden, J.; Deschamps, A.; Proulx-Bourque, J.S.; Warren, S. The River Ice Automated Classifier Tool (RIACT). Presented at the CGU HS Committee on River Ice Processes and the Environment. In Proceedings of the 19th Workshop on River Ice, Whitehorse, YT, Canada, 9–12 July 2017. [ Google Scholar ]
• Rob, B.; Khan, A.A.; Lynch, M.; English, J.; Puestow, T. River Ice Monitoring: An Integrated Space, Airborne and In-Situ Sensing Approach. In Proceedings of the Geomatics Atlantic 2020, Virtual Conference, 25–26 November 2020. [ Google Scholar ]
• Lynch, M.; Briggs, R.; English, J.; Khan, A.A.; Khan, H.; Puestow, T. Operational Monitoring of River Ice on the Churchill River, Labrador. In Proceedings of the 21st Workshop on the Hydrolics of Ice Covered Rivers, Saskatoon, SK, Canada, 29 August–1 September 2021. [ Google Scholar ]
• Lynch, M.; Warren, S.; Power, D. Investigation of Leading Indicators for Pipeline Encroachment. Contract ; Prepared for the Operations & Integrity Technical Committee of Pipeline Research Council International; C-CORE: St. John’s, NL, Canada, 2015. [ Google Scholar ]
• CCORE. Churchill River Ice Thickness Monitoring—SIMBA, 2020–2021 Ice Season ; C-CORE: St. John’s, NL, Canada, 2021. [ Google Scholar ]
• Geldsetzer, T.; van der Sanden, J.; Drouin, H. Advanced SAR Applications for Canada’s River and Lake Ice. In Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada, 24–29 July 2011; pp. 3168–3170. [ Google Scholar ]
• NRCan River Ice in Canada—Product Specifications. 2020. Available online: https://open.canada.ca/data/en/dataset/8ca6f047-ddef-43d7-81c2-47654f4c69bd (accessed on 11 July 2024).
• EEA Pan-European High-Resolution Snow & Ice Monitoring of the COPERNICUS Land Monitoring Service—Production of Basics Products ; In Product User Manual for Ice Products; European Environment Agency: Copenhagen, Denmark, 2021.
• EEA Pan-European High-Resolution Snow & Ice Monitoring (HR-S&I) of the Copernicus Land Monitoring Service. In Algorithm Theoretical Basis Document FFor Ice Products Based on Sentinel-1 & Sentinel-2 ; European Environment Agency: Copenhagen, Denmark, 2022.
• EEA Pan-European High-Resolution Snow & Ice Monitoring (HR-S&I) of the Copernicus Land Monitoring Service. In Algorithm Theoretical Basis Document FFor Ice Products Based on Sentinel-2 ; European Environment Agency: Copenhagen, Denmark, 2023.
• Abdelkader, M.; Bravo Mendez, J.H.; Temimi, M.; Brown, D.R.N.; Spellman, K.V.; Arp, C.D.; Bondurant, A.; Kohl, H. A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics. Remote Sens. 2024 , 16 , 1368. [ Google Scholar ] [ CrossRef ]
• Stewart, E.J.; Liggett, D.; Lamers, M.; Ljubicic, G.; Dawson, J.; Thoman, R.; Haavisto, R.; Carrasco, J. Characterizing Polar Mobilities to Understand the Role of Weather, Water, Ice and Climate (WWIC) Information. Polar Geogr. 2020 , 43 , 95–119. [ Google Scholar ] [ CrossRef ]
• Zakharov, I.; Bobby, P.; Power, D.; Warren, S.; Howell, M. Satellite-Based Identification and Characterization of Extreme Ice Features: Hummocks and Ice Islands. Remote Sens. 2023 , 15 , 4065. [ Google Scholar ] [ CrossRef ]
• Łoś, H.; Osińska-Skotak, K.; Pluto-Kossakowska, J.; Bernier, M.; Gauthier, Y.; Pawłowski, B. Performance Evaluation of Quad-Pol Data Compare to Dual-Pol SAR Data for River Ice Classification. Eur. J. Remote Sens. 2019 , 52 , 79–95. [ Google Scholar ] [ CrossRef ]
• Lincoln, S. River Ice Spotter Network Training Information ; National Weather Service: Silver Spring, MD, USA, 2023. [ Google Scholar ]
• Elhadi, N.; Lockhart, J. New Brunswick River Ice Manual ; Communication New-Brunswick: Fredericton, NB, Canada, 1989. [ Google Scholar ]
• Gherboudj, I.; Bernier, M.; Leconte, R. A Backscatter Modeling for River Ice: Analysis and Numerical Results. IEEE Trans. Geosci. Remote Sens. 2010 , 48 , 1788–1798. [ Google Scholar ] [ CrossRef ]
• Ashton, G.D. River and Lake Ice Engineering ; Water Resources Publications: Littleton, CO, USA, 1986; ISBN 978-0-918334-59-6. [ Google Scholar ]
• Wazney, L.; Clark, S.; Wall, A. Freeze-up Jam Observations on the Dauphin River. In Proceedings of the Committee on River Ice Processes and the Environment (CRIPE), Whitehorse, YT, Canada, 9 July 2017. [ Google Scholar ]
• Zhao, S.-X.; Wang, W.-J.; Shi, X.-H.; Zhao, S.-N.; Wu, Y.-J.; Quan, Q.; Li, C.; Szydlowski, M.; Li, W.; Kolerski, T. Freeze-Up Ice Jam Formation in the River Bend, a Case Study on the Inner Mongolia Reach of Yellow River. Crystals 2021 , 11 , 631. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Zakharov, I.; Puestow, T.; Khan, A.A.; Briggs, R.; Barrette, P. Review of River Ice Observation and Data Analysis Technologies. Hydrology 2024 , 11 , 126. https://doi.org/10.3390/hydrology11080126

Zakharov I, Puestow T, Khan AA, Briggs R, Barrette P. Review of River Ice Observation and Data Analysis Technologies. Hydrology . 2024; 11(8):126. https://doi.org/10.3390/hydrology11080126

Zakharov, Igor, Thomas Puestow, Amir Ali Khan, Robert Briggs, and Paul Barrette. 2024. "Review of River Ice Observation and Data Analysis Technologies" Hydrology 11, no. 8: 126. https://doi.org/10.3390/hydrology11080126

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

• Open access
• Published: 24 August 2024

Knowledge and practices of youth awareness on death and dying in school settings: a systematic scoping review protocol

• Emilie Allard 1 , 2 ,
• Clémence Coupat 1 , 2 ,
• Sabrina Lessard 3 , 4 ,
• Noémie Therrien 5 ,
• Claire Godard-Sebillotte 6 , 7 , 8 ,
• Dimitri Létourneau 1 , 2 ,
• Olivia Nguyen 2 , 9 , 10 ,
• Andréanne Côté 2 , 9 , 10 ,
• Gabrielle Fortin 11 , 12 ,
• Serge Daneault 9 , 13 ,
• Maryse Soulières 3 , 14 ,
• Josiane Le Gall 4 , 15 , 16 &
• Sylvie Fortin 4 , 15 , 17  

Systematic Reviews volume  13 , Article number:  220 ( 2024 ) Cite this article

11 Accesses

Metrics details

Awareness-raising and education have been identified as strategies to counter the taboo surrounding death and dying. As the favoured venue for youth education, schools have an essential role to play in informing future decision-makers. However, school workers are not comfortable addressing the subjects of death and dying, which, unlike other social issues, have no guidelines to influence awareness of these subjects in youth.

To systematically explore the knowledge and practices on raising awareness about death and dying in schools, the viewpoints of the people involved (young people, school workers; parents), and the factors that either promote or hinder awareness practices.

The scoping review method of Levac and Colquhoun (Implement Sci 5(1):69, 2010) will be used. Using a combination of keywords and descriptors, a body of literature will be identified through 15 databases and through grey literature searches, manual searches, consultation of key collaborators, and the list of relevant literature. Publications since 2009 will be selected if they relate directly to awareness-raising about death and dying in schools. Writings will be selected and extracted by two independent people, and conflicts resolved by consensus. The extracted data will be synthesized using a thematic analysis method. Experts from a variety of disciplines (health sciences, humanities, social sciences, and education) will be consulted to enhance the interpretation of the preliminary results. Results will be presented in narrative form and will include tables and diagrams.

The results of this scoping review will contribute to the development of educational practices adapted to young people and to the identification of future avenues of research on awareness of death and dying.

Peer Review reports

The recent report of the Lancet Commission on the Value of Death [ 1 ] reveals the uneasy relationship between the twenty-first century society, particularly in affluent countries, with death and dying, i.e. the process surrounding the death of a person, including the idea of our own death. The authors of the report emphasize that there is still much to be done to reverse people’s often negative representations of death and dying and the lack of knowledge, discomfort, anxieties, and sometimes even taboos regarding these issues. Thus, although death and dying are common and inescapable realities for all human beings, addressing these phenomena openly in Western society can be difficult, particularly since the subject is often emotionally laden and sometimes considered taboo [ 1 , 2 ]. This difficulty is even more acute when dealing with children and adolescents, Footnote 1 where factors such as age, developmental stage, personality, or religious beliefs [ 3 , 4 , 5 ] can shape their understanding of dying and death. What’s more, adults are afraid to broach these subjects with young people for fear of causing them suffering and anxiety as well as the fact that they may have their own anxiety about the subject [ 3 , 6 , 7 ].

Yet researchers have shown that young people construct their own understanding of these phenomena, within the societal and cultural context in which they grow up [ 8 ]. Young people come into contact with death and dying in various ways. They may experience bereavement directly, through the death of a close relative (grandparent, parent, friend) or companion animal. Death is also represented in the world of television, media, cartoons [ 9 , 10 , 11 ], books [ 3 , 5 ] and video games [ 12 ].

One way to counter the taboo surrounding death and dying is through awareness-raising and education [ 1 , 13 ]. Death literacy is considered to stem from experiences and learnings about death and dying that help improve individuals’ and communities’ ability to act in these situations [ 14 ]. To become death literate, it is important to support educational initiatives on the subject of death, so young people—considered as social actors and citizens of tomorrow—can be better equipped to face death, understand the situations and care involved with it, and participate in accompanying and supporting those going through these situations.

As the favoured venue for educating youth, schools can play a key role in death literacy. During a talk on end-of-life issues given by the principal investigator (PI) to elementary school children, it was observed that they appreciated being able to openly discuss their views on death and dying, which are largely influenced by their personal experiences (e.g. death of a grandparent) and social interactions (e.g. social media, friends). On the other hand, school workers say they are ill-equipped to tackle this issue with youth, not knowing what to say nor how to approach it [ 15 ]. In a socially and culturally diverse environment that includes young people of different origins, beliefs, migratory statuses, and life experiences, talking about death can be even more sensitive, since it not only involves the abovementioned taboo but also a plurality of cultural and religious beliefs surrounding these final moments of life [ 16 ]. School workers also report being concerned about how parents will react to this topic, which is considered a social taboo and is influenced by the cultural aspects, beliefs, and values held by each family.

To our knowledge, there are no resources for school workers to initiate a dialogue with students about death and dying. However, other social issues (e.g. sexual and gender identity) have been incorporated into the educational curricula in some countries, drawing on government and international guidelines [ 17 ]. While ad hoc initiatives concerning death and dying are being produced [ 18 ], the state of knowledge and practices on raising awareness about these subjects among school-aged young people needs to be clarified. This would make it possible to identify and implement actions that could support the training of school workers in addressing death and dying with youth as well as practices contributing to the death literacy of our future decision-makers.

Goals of the review

To guide the development of cross-sectoral (education, health, and social sciences) death literacy interventions for children and staff in school settings, this systematic scoping review will explore the state of knowledge and practices in raising awareness of death and dying among young people in schools, the viewpoints of the people involved (young people, school workers, parents), and the factors that promote or hinder such awareness-raising. In fact, this type of review will make it possible to conduct an extensive, exhaustive, and comprehensive examination and analysis, including publications of a variety of methods and grey literature. This thereby enables the identification of practices that can inform the development of awareness-raising interventions.

Levac [ 19 ] scoping review method will be used. This method comprises six steps: (1) identify the review questions, (2) identify the literature, (3) select the literature, (4) extract data, (5) report the result, and (6) consult stakeholders.

This protocol is registered with Open Science Framework (OSF) [ 20 ] and based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA- ScR) (see supplementary file 1). As the scoping review is carried out iteratively, this protocol will serve as the starting point for documenting adjustments and changes to the method.

Step 1: Identify the review questions

The following questions will guide the scoping review:

How do we raise awareness on death and dying in the school settings?

What are the views of young people, parents, and school workers on raising awareness about death and dying in the school settings?

What factors help or hinder this awareness-raising in the school settings?

Step 2: Identify the literature

Inclusion and exclusion criteria.

Literature that meets the population-concept-context (PCC) criteria will be included [ 21 ].

Two types of population have been identified to answer the questions posed by the scoping review. The main population is youth in schools, i.e. children or teenagers attending elementary or high schools, who are the targets of awareness-raising practices.

Within the selected literature, the scoping review process will also focus on extracting the viewpoints of the people involved with these young people, notably parents, teachers, and other practitioners in school environments (nurse, principal, etc.). These make up our secondary population.

The central concept of this scoping review refers to raising awareness of death and dying, i.e. arousing interest and offering relevant, scientifically informed information to support individual and social reflection on the subject. This concept thus intersects with death education and literacy. As previously mentioned, death literacy results from individuals’ experiences and learnings, enabling them to project themselves into the future (prospection), to better understand and improve experiences around death and dying [ 14 ].

Considering the plurality of terms used to define awareness-raising, education, and literacy on death and dying, the literature include in this scoping review must report on how young people are exposed to and led to reflect on these concepts in a school setting. Dying refers to the physical, psychosocial, cultural, and spiritual processes that lead to a person’s death [ 22 ]. This concept thus incorporates care and practices, as well as the losses and bereavement associated with this period of human existence. Therefore, are included the publications on the full range of end-of-life care, including palliative care, end-of-life care, medical aid in dying, and assisted suicide. Death, the cessation of vital functions, marks the end of life and thus also the end of the dying process. As death and dying are universal social phenomena, no restrictions are placed on health status, context (natural disaster, war, other tragedies, etc.), or the age of the deceased. However, the following types of publications are excluded: those on suicide prevention, those on serious health conditions in which death or the end of life is not a central issue (e.g. chronic illness), and those discussing bereavement not related to death (e.g. divorce).

Publications will be considered if they deal with raising awareness about death and dying explicitly and exclusively in a school setting. Given the differences in educational structures between countries, the school settings included will be all elementary and secondary education environments (or their equivalents). Excluded will be publications about informal education settings (e.g. family, daycare), postsecondary education settings, and activities taking place outside the institutional framework of a school (e.g. extracurricular or community activities).

Type of records

The search strategy will be limited to publication in English or French, but without restriction on the place of study. Over the last few decades, the evolution of technology has led to changes in teaching methods in Western societies. The number of writings on technology in education has boomed since 2009, reflecting the implementation and adaptation of the school environment to the digital age, the development of information technologies, the introduction of the Internet in various communities, the development of distance learning, and generational changes [ 23 , 24 , 25 ]. To ensure that this search reflects the challenges of contemporary social, pedagogical, and societal change, only publications from January 1, 2009, onwards will be included.

All types of literature will be considered, including primary studies of various designs (e.g. experimental, quasi-experimental, observational, qualitative, mixed), literature reviews (e.g. meta-analyses, systematic reviews, narrative reviews), grey literature (e.g. theses, research reports, models of educational practice), and theoretical publication dealing specifically with the subject of raising young people’s awareness of dying and death in the school environment. The following are excluded: blogs, media entries, personal opinions, book reviews, letters to the editor, editorials, conference abstracts, and research protocols.

Step 3: Select the literature

Information sources.

Four categories of information sources will be used to identify the literature.

Databases : The following databases will be surveyed: CINAHL Complete (Cumulative Index to Nursing and Allied Health Literature) (EBSCO), MEDLINE (Medical Literature Analysis and Retrieval System Online) (Ovid), EBM (Evidence-Based Medicine) Reviews Cochrane (Ovid), JBI EBP (Evidence-Based Practice) Database (Ovid), PsycINFO (Ovid), Web of Science (Clarivate), Global Health (OVID), Sociological Abstracts (ProQuest), Social Sciences Abstracts (EBSCO), Family Studies Abstracts (EBSCO), Social Services Abstracts (ProQuest), Social Work Abstracts (EBSCO), Erudit, CAIRN, and PubPsy.

Grey literature : A grey literature search will be conducted systematically in the following databases: Dissertations & Theses Global (ProQuest) and Google Scholar.

Reference searching : The reference list of the publications included in the review will be examined to find other relevant sources. The same will be done with the tables of contents of journals that have published key publications.

Key authors and collaborators : The key authors and collaborators to this project will be contacted by email to identify unindexed literature or unpublished practice guidelines, to verify the completeness of the search strategy.

Search strategy

In collaboration with a health sciences librarian, a literature search strategy was developed using a combination of the three concepts (see Table  1 ): (1) death and dying, (2) youth, and (3) school. Initially developed for the CINAHL-Complete (EBSCO) database, the search strategy was subsequently adapted for the other databases. The optimization of the search strategy by descriptors and keywords took place over a 4-month period, between January and May 2023. Keywords are searched for in titles, abstracts, and keywords, to identify publications not indexed in database thesauri. The Medical Subject Headings (MeSH) terms used for MEDLINE are presented in Table  1 , and supplementary file 2 presents all the search strategies used.

Here is the final strategy for MEDLINE database: (((exp Death/ or Palliative care/ or Terminal care/ or bereavement/ or grief/ or exp Hospice Care/ or exp Hospices/ or exp Euthanasia/ or Suicide, Assisted/ or Attitude to Death/ or Funeral Rites/) or ((Death* or Dying or Palliati* or Hospice* or Euthanasia or Bereav* or Bereft or Grief or Grieving or Mourning or Funeral* or ((Terminal* adj1 (care OR ill*)) or (suicide adj2 assist*)) or "End of life" or "Supportive care").ab,kf,ti.)) AND ((Child/ or Adolescent/) or ((Youth* or Child* or Boy* or Girl* or Kid or Kids or Adolescen* or Teen*).ab,kf,ti.)) AND ((Schools/ or Students/ or School Teachers/ or Teaching/ or exp Curriculum/) or (School* or Kindergarten* or Curriculum* or Teacher* or Pupil* or ((Education or Student*) adj1 (Primary or Secondary or Elementary)).ab,kf,ti.)) AND (limit to yr = "2009—2023")).

Study records

Data management.

The literature obtained through this search strategy will be imported into the Covidence systematic review assistance tool (Veritas Health Innovation Ltd., Melbourne, Australia), which removes duplicates and allows the literature selection process to be done independently by team members.

Selection process

To calibrate the selection process and define the exclusion criteria, a committee, made up of several members of the research team, will select 15% of the literature randomly chosen. Selection tools will be produced following this calibration process, and the rest of the selection will be carried out by four members. The selection process will begin with a reading of each title and abstract. To be included in this first stage, a publication must be independently accepted by two people. Conflicts will be discussed and resolved by consensus, if necessary, involving a team member from outside the selection process.

The second stage of the selection process is the full-text review by two independent team members. Using five full texts, chosen for their differences (e.g. type of records, designs), a calibration process will be undergone by several team members to clarify inclusion and exclusion reasons. At this stage, reasons for exclusion will be documented. Publications deemed uncertain, and conflicts will again be discussed by the selection team, to reach a consensus resolution. A unique identifier will be assigned to the publications included at the end of the selection process.

Step 4: Extract data

As for the selection process, the extraction will be carried out by a subgroup of the research team after a calibration process to fine-tune the extraction tool. The calibration process will be the extraction of two publications by the team members involved in the extraction process to establish agreement. After the calibration process, each publication will be extracted by one person, and the extraction will be validated by another team member. Uncertainties will be discussed as a team. Using a template built in Covidence, the following data will be extracted, if mentioned, and depending on the nature of the selected publication.

General data: Title, publication year, authors’ names, discipline of first author, country, type of writing (e.g. literature review, primary study, practice summary), purpose, and objectives

Theoretical data: The philosophical stance and frame of reference guiding the project or the practice

Data on interventions/practices: Type of awareness-raising practice (e.g. conference presentation, curriculum), characteristics (e.g. time, subjects), barriers and facilitators, people involved, and their characteristics

Methodological data: Research design, setting, sample (number, inclusion, and exclusion criteria), participant characteristics (e.g. age, grade), data collection and analysis methods, strengths, and limitations identified by the authors

Results data: Various stakeholders’ viewpoints on awareness-raising practices, influencing factors, consequences or impacts of the practice, and suggestions for improvement

Assessing the methodological quality of the selected literature is not a required step according to Levac [ 19 ]. In this scoping review, methodological quality will not be assessed, due to the expected diversity of publications from both research and practice models. Nevertheless, the data extracted, methods used, and transferability of the practices reported will be considered critically. During the consultation phase, partners and collaborators will be invited to comment on the results.

Step 5: Report the results

The selection process will be illustrated using a diagram from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) [ 26 ]. The extracted data will be analysed using the content analysis method of Miles and Huberman [ 27 ], which comprises three steps: (1) condensing the data (coding), (2) finding similarities and differences, and (3) drawing conclusions (identifying themes and subthemes). The results will be presented in narrative form, integrating results from a variety of publications, with tables and graphs to identify the specific features of each. The presentation of the results will answer the three research questions.

Step 6: Consult stakeholders

The sixth step is deemed optional by the method designers, but given the nature of our scoping review, a great deal of time will be spent consulting external parties to identify awareness-raising practices. First, project partners and collaborators will be consulted to identify additional or unpublished texts on raising youth awareness of death and dying. The list of identified references, together with the inclusion and exclusion criteria, will be shared with them so they can suggest additional references, particularly from the grey literature. When a first version of the result synthesis is produced, it will be shared with them to obtain their view, given their experience with and expertise on the subject. Specific questions will be sent to them in writing (email) or via a telephone discussion with a member of the research team. These consultations will enhance the interpretation of the results.

To the best of our knowledge, no publication exists to guide the development of awareness-raising practices on death and dying in schools. This scoping review hopes to identify promising practices along with the factors influencing youth awareness-raising and the challenges associated with such practices. This project is also in line with the recommendations of the recent report of the Lancet Commission on the Value of Death [ 1 ], which stresses the importance of educating the population in order to transform the social view of death and dying and to recognize these phenomenon as integral parts of the human experience. The results can then be used to guide school staff in setting up educational activities in line with children’s age and development stage. The project’s conclusions will offer concrete recommendations to decision-makers in educational environments and governments on how to incorporate these themes into the educational pathways of tomorrow’s citizens.

The limitations of this scoping review include the lack of assessment of the quality of the selected literature, which may influence the recommendations that emerge. Nevertheless, the aim of this scoping review is to consider the state of knowledge and practices in the field of awareness-raising of death and dying in school settings, which does not require an assessment of the quality of the literature reviewed. The combination of multiple sources of information and types of writings is a challenge for such a systematic review but is also a source of richness. In addition to using a systematic method and complying with the PRISMA-ScR recommendations, the strengths of this scoping review lie in the quality and diversity of the research team, which includes several researchers with cross-sectoral expertise (education, health, humanities, and social sciences) complementary to the study, as well as experience in carrying out systematic knowledge synthesis. The team works closely with a librarian and with local and international collaborators and partners carrying out awareness-raising activities among the target population.

Availability of data and materials

See OSF registration:  https://doi.org/10.17605/OSF.IO/EHY8T .

Hereinafter referred to as “young people” or “youth”, with the aim of being inclusive, without any judgements about age

Sallnow L, Smith R, Ahmedzai SH, Bhadelia A, Chamberlain C, Cong Y, et al. Report of the Lancet Commission on the value of death: bringing death back into life. Lancet. 2022;399(10327):837–84.

Article   PubMed   PubMed Central   Google Scholar  

Fortin S, Le Gall J. Présentation: fin de vie et mourir contemporains. Anthropol Soc. 2021;45(1–2):15–24.

Google Scholar  

Longbottom S, Slaughter V. Sources of children’s knowledge about death and dying. Philos Trans R Soc B Biol Sci. 2018;373(1754):20170267.

Article   Google Scholar  

McClement S, Stenekes S. Comment parler d’une maladie grave à un enfant ou à un adolescent. Portail Palliatif Canadien; 2023. Available from: https://www.virtualhospice.ca/fr_CA/Main+Site+Navigation/Home/Topics/Topics/Communication/Talking+with+Children+and+Youth.aspx .

Mahon MM. Death in the lives of children. In: Talwar V, Harris PL, Schleifer M, editors. Children’s understanding of death: from biological to religious conceptions. New York: Cambridge University Press; 2011. p. 61–97.

Chapter   Google Scholar  

Hanna JR, McCaughan E, Semple CJ. Challenges and support needs of parents and children when a parent is at end of life: a systematic review. Palliat Med. 2019;33(8):1017–44.

Article   PubMed   Google Scholar  

Rapa E, Hanna JR, Mayland CR, Mason S, Moltrecht B, Dalton LJ. Experiences of preparing children for a death of an important adult during the COVID-19 pandemic: a mixed methods study. BMJ Open. 2021;11(8):e053099.

Ahmadi F, Ristiniemi J, Linblad I, Schiller L. Perceptions of death among children in Sweden. Int J Child Spirit. 2019;24(4):415–33.

Rabatel A, Florea M-L. Re-présentations de la mort dans les médias d’information. Quest Commun. 2011;19:7–28.

Le Guay D. Représentation actuelle de la mort dans nos sociétés: Les différents moyens de l’occulter. Études sur la mort. 2008;134(2):115–23.

Julier-Costes M. Le paradigme du déni social de la mort à l’épreuve des séries télévisées. Mise en scène et mise en sens de la mort. Études sur la mort. 2011;139(1):145–63.

Ernst G, Bergeron P, Laflamme D. Mort, jeux vidéo et mondes virtuels. Frontières. 2016;28(2).  https://doi.org/10.7202/1040191ar .

Graham-Wisener L, Toner P, Leonard R, Groarke JM. Validation of the Death Literacy Index and benchmarking of death literacy level in a representative UK population sample. BMC Palliative Care. Preprint.

Noonan K, Horsfall D, Leonard R, Rosenberg J. Developing death literacy. Prog Palliat Care. 2016;24(1):31–5.

Talwar V. Talking to children about death in educational settings. In: Talwar V, Harris PL, Schleifer M, editors. Children’s understanding of death: from biological to religious conceptions. New York: Cambridge University Press; 2011. p. 98–115.

Hirsch S, Audet G, Turcotte M. Vivre ensemble. Aborder les sujets sensibles avec les élèves: Centre d’intervention pédagogique en contexte de diversité. Commission scolaire Marguerite-Bourgeois; 2015. Available from: https://www.cipcd.ca/wp-content/uploads/2014/04/CSMB_-Guide_sujets-sensibles_final..pdf .

Martino W. Supporting transgender students and gender-expansive education in schools: investigating policy, pedagogy, and curricular implications. Teach Coll Rec. 2022;124(8):3–16.

Papazian-Zohrabian G, Mamprin C, Lemire V, Turpin-Samson A. Prendre en compte l’expérience pré-, péri- et post-migratoire des élèves réfugiés afin de favoriser leur accueil et leur expérience socioscolaire. Alterstice. 2018;8(2):101–16.

Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5(1):69.

Knowledge and practices of youth awareness on death and dying in school settings: protocol registration. Open Science Framework. 2023.  https://doi.org/10.17605/OSF.IO/EHY8T .

Peters M, Godfrey C, McInerney P, Munn Z, Tricco A, Khalil H. Scoping reviews. In: Aromataris E, Munn Z, editors. JBI Manual for evidence synthesis. Joanna Briggs Institute; 2020. https://doi.org/10.46658/JBIRM-19-01 .

Kellehear A. On dying and human suffering. Palliat Med. 2009;23(5):388–97.

Article   CAS   PubMed   Google Scholar  

Chauhan S. A meta-analysis of the impact of technology on learning effectiveness of elementary students. Comput Educ. 2017;105:14–30.

Rahmatullah AS, Mulyasa E, Syahrani S, Pongpalilu F, Putri RE. Digital era 40: the contribution to education and student psychology. Linguist Cult Rev. 2022;6(S3):89–107.

Hashim H. Application of technology in the digital era education. Int J Res Couns Educ. 2018;2(1):1.

Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73.

Miles M, Huberman M, Saldana J. Qualitative data analysis: a sourcebook of new methods. 3rd ed. Beverly Hills: Sage Publications; 2014. p. 263.

Download references

Acknowledgements

The authors acknowledge the contribution of Assia Mourid, Health Sciences Librarian at the Université de Montréal, for her help in building the search strategy. The authors acknowledge the contribution of Mélanie Vachon and Geneviève Audet in drafting the project’s funding protocol.

The authors would like to thank the Réseau québécois de recherche en soins palliatifs et de fin de vie (RQSPAL) for the funding granted.

Author information

Authors and affiliations.

Faculty of Nursing, Université de Montréal, Montréal, QC, Canada

Emilie Allard, Clémence Coupat & Dimitri Létourneau

Research Center, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l’Île-de-Montréal (CIUSSS NIM), Montréal, QC, Canada

Emilie Allard, Clémence Coupat, Dimitri Létourneau, Olivia Nguyen & Andréanne Côté

Centre for Research and Expertise in Social Gerontology, Centre Intégré Universitaire de Santé et de Services Sociaux Centre-Ouest-de-l’Île-de-Montréal (CIUSSS-CCOMTL), Montréal, QC, Canada

Sabrina Lessard & Maryse Soulières

Department of Anthropology, Université de Montréal, Montréal, QC, Canada

Sabrina Lessard, Josiane Le Gall & Sylvie Fortin

Faculty of Medicine, Université de Montréal, Montréal, QC, Canada

Noémie Therrien

Division of Geriatric Medicine, McGill University Health Centre (MUHC), Montréal, QC, Canada

Claire Godard-Sebillotte

Division of Geriatrics, Department of Medicine, McGill University, Montréal, QC, Canada

MUHC Research Institute, Montréal, QC, Canada

Department of Family and Emergency Medicine, Université de Montréal, Montréal, QC, Canada

Olivia Nguyen, Andréanne Côté & Serge Daneault

Palliative Care Services, CIUSSS NIM, Montréal, QC, Canada

Olivia Nguyen & Andréanne Côté

School of Social Work and Criminology, Université Laval, Québec, QC, Canada

Gabrielle Fortin

Research Center, CHU de Québec-Université Laval, Québec, QC, Canada

Research Center, Montreal University Institute of Geriatrics, Montréal, QC, Canada

Serge Daneault

School of Social Work, Université de Montréal, Montréal, QC, Canada

Maryse Soulières

Research Center SHERPA, CIUSSS-CCOMTL, University Institute on Immigration, Diversity, and Health, Montréal, QC, Canada

Josiane Le Gall & Sylvie Fortin

Montreal University Centre for Ethnic Studies (CEETUM), Montréal, QC, Canada

Josiane Le Gall

Department of Pediatrics, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada

Sylvie Fortin

You can also search for this author in PubMed   Google Scholar

Contributions

All authors fulfil the three criteria for authorship listed in BMC. Here is the CRediT statement based on the taxonomy of Brand et al. (2015): EA, conceptualization, methodology, software, validation, investigation, resources, writing — original draft, review and editing, project administration, supervision, and funding acquisition; CC, methodology, software, validation, investigation, resources, writing — original draft, review and editing, and visualization; SL, conceptualization, methodology, investigation, resources, writing — original draft, and review and editing; NT, methodology, investigation, resources, writing — original draft, and review and editing; CG-S, conceptualization and writing — review and editing; DL, conceptualization, methodology, and writing — review and editing; ON, conceptualization and writing — review and editing; AC, conceptualization, methodology, and writing — review and editing; GF, conceptualization, methodology, and writing — review and editing; SD, conceptualization and writing — review and editing; MS, conceptualization, methodology, and writing — review and editing; JLG, conceptualization, methodology, and writing — review and editing; and SF, conceptualization, methodology, and writing — review and editing.

Corresponding author

Correspondence to Emilie Allard .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

13643_2024_2635_moesm1_esm.docx.

Supplementary Material 1. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist

Supplementary Material 2: Database.

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Allard, E., Coupat, C., Lessard, S. et al. Knowledge and practices of youth awareness on death and dying in school settings: a systematic scoping review protocol. Syst Rev 13 , 220 (2024). https://doi.org/10.1186/s13643-024-02635-9

Download citation

Received : 31 October 2023

Accepted : 13 August 2024

Published : 24 August 2024

DOI : https://doi.org/10.1186/s13643-024-02635-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Death literacy
• Intervention
• Systematic review

Systematic Reviews

ISSN: 2046-4053

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

The diagnosis performance of [ 18 F]FDG PET/CT, MRI, and CT in the diagnosis of mandibular invasion in oral/oropharyngeal carcinoma: a head-to-head comparative meta-analysis

• Published: 26 August 2024

Cite this article

• Siqi Zhao 1 &
• Xiao Li 2  

This research synthesis investigates the diagnostic performance of [ 18 F]FDG PET/CT, MRI, and CT in detecting mandibular invasion in patients with oral and oropharyngeal cancer.

An extensive literature review was conducted using PubMed and Embase, targeting studies up to March 2024 that examined the diagnostic capabilities of [ 18 F]FDG PET/CT, MRI, and CT for oral and oropharyngeal cancer patients. Sensitivity and specificity were calculated using the DerSimonian and Laird random-effects model with adjustments via the Freeman-Tukey double arc sine transformation. Study quality was assessed with the QUADAS-2 tool.

This meta-analysis synthesized data from 24 studies involving 1376 participants to compare the diagnostic performance of CT, MRI, and [ 18 F]FDG PET/CT for mandibular invasion in oral and oropharyngeal cancer patients. The results showed closely matched sensitivity and specificity among the technologies: CT pooled a sensitivity of 0.80 and specificity of 0.85, while MRI exhibited a slightly better sensitivity at 0.87 but lower specificity at 0.81, with the differences not reaching statistical significance (all P  > 0.05). [ 18 F]FDG PET/CT also demonstrated comparable performance, achieving a sensitivity of 0.77 versus CT’s 0.72 and a specificity of 0.82 versus CT’s 0.93, alongside matching MRI’s sensitivity at 0.86 and a specificity of 0.68 versus MRI’s 0.75, with all comparisons showing no significant disparities (all P  > 0.05).

Conclusions

The meta-analysis concludes that there was no statistically significant difference in diagnostic performance between [ 18 F]FDG PET/CT, CT and MRI. Further research with prospective comparative trials is recommended to validate these findings in new clinical cohorts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Explore related subjects

• Medical Imaging

Data availability

No datasets were generated or analysed during the current study.

Moro JDS, Maroneze MC, Ardenghi TM, Barin LM, Danesi CC (2018) Oral and oropharyngeal cancer: epidemiology and survival analysis. Einstein (Sao Paulo) 16:eAO4248. https://doi.org/10.1590/s1679-45082018ao4248

Article   PubMed   Google Scholar  

Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I et al (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. https://doi.org/10.3322/caac.21834

Brown JS, Lowe D, Kalavrezos N, D’Souza J, Magennis P, Woolgar J (2002) Patterns of invasion and routes of tumor entry into the mandible by oral squamous cell carcinoma. Head Neck 24:370–383. https://doi.org/10.1002/hed.10062

Hanasono MM (2014) Reconstructive surgery for head and neck cancer patients. Adv Med 2014:795483. https://doi.org/10.1155/2014/795483

Article   PubMed   PubMed Central   Google Scholar  

Jimi E, Furuta H, Matsuo K, Tominaga K, Takahashi T, Nakanishi O (2011) The cellular and molecular mechanisms of bone invasion by oral squamous cell carcinoma. Oral Dis 17:462–468. https://doi.org/10.1111/j1601-0825201001781x

Article   CAS   PubMed   Google Scholar  

Uribe S, Rojas LA, Rosas CF (2013) Accuracy of imaging methods for detection of bone tissue invasion in patients with oral squamous cell carcinoma. Dentomaxillofac Radiol 42:20120346. https://doi.org/10.1259/dmfr.20120346

Article   CAS   PubMed   PubMed Central   Google Scholar  

Brown JS, Lewis-Jones H (2001) Evidence for imaging the mandible in the management of oral squamous cell carcinoma: a review. Br J Oral Maxillofac Surg 39:411–418. https://doi.org/10.1054/bjom20010717

Tantiwongkosi B, Yu F, Kanard A, Miller FR (2014) Role of (18)F-FDG PET/CT in pre and post treatment evaluation in head and neck carcinoma. World J Radiol 6:177–191. https://doi.org/10.4329/wjr.v6.i5.177

Brown JS, Griffith JF, Phelps PD, Browne RM (1994) A comparison of different imaging modalities and direct inspection after periosteal stripping in predicting the invasion of the mandible by oral squamous cell carcinoma. Br J Oral Maxillofac Surg 32:347–359. https://doi.org/10.1016/0266-4356(94)90024-8

Pfister DG, Spencer S, Adelstein D, Adkins D, Anzai Y, Brizel DM et al (2020) Head and neck cancers, version 2.2020, NCCN clinical practice guidelines in oncology. J Natl Compr Cancer Netw 18:873–898. https://doi.org/10.6004/jnccn.2020.0031

Article   Google Scholar  

Wu J, Wang Y, Liao T, Rao Z, Gong W, Ou L et al (2021) Comparison of the relative diagnostic performance of [(68)Ga]Ga-DOTA-FAPI-04 and [(18)F]FDG PET/CT for the detection of bone metastasis in patients with different cancers. Front Oncol 11:737827. https://doi.org/10.3389/fonc.2021.737827

Lee YC, Jung AR, Kwon OE, Kim EJ, Hong IK, Lee JW et al (2019) Comparison of computed tomography, magnetic resonance imaging, and positron emission tomography and computed tomography for the evaluation bone invasion in upper and lower gingival cancers. J Oral Maxillofac Surg 77:875e1. https://doi.org/10.1016/j.joms.2018.12.010

Mehta AS, Nagarkar R, Bodhey NM, Gupta NK, Satpute RK (2024) Clinical, radiological, and pathological correlation of mandibular invasion in carcinoma Bucco-alveolar complex. Indian J Surg Oncol. https://doi.org/10.1007/s13193-024-01903-3

Gu DH, Yoon DY, Park CH, Chang SK, Lim KJ, Seo YL et al (2010) CT, MR, (18)F-FDG PET/CT, and their combined use for the assessment of mandibular invasion by squamous cell carcinomas of the oral cavity. Acta Radiol 51:1111–1119. https://doi.org/10.3109/028418512010520027

Abd El-Hafez YG, Chen CC, Ng SH, Lin CY, Wang HM, Chan SC et al (2011) Comparison of PET/CT and MRI for the detection of bone marrow invasion in patients with squamous cell carcinoma of the oral cavity. Oral Oncol 47:288–295. https://doi.org/10.1016/j.oraloncology.2011.02.010

McGrath TA, Alabousi M, Skidmore B, Korevaar DA, Bossuyt PMM, Moher D et al (2017) Recommendations for reporting of systematic reviews and meta-analyses of diagnostic test accuracy: a systematic review. Syst Rev 6:194. https://doi.org/10.1186/s13643-017-0590-8

Roysri K, Chotipanich C, Laopaiboon V, Khiewyoo J (2014) Quality assessment of research articles in nuclear medicine using STARD and QUADAS-2 tools. Asia Ocean J Nuclear Med Biol 2:120–126

Google Scholar  

Bouhir S, Mortuaire G, Dubrulle-Berthelot F, Leroy X, Deken-Delannoy V, Rysman B et al (2019) Radiological assessment of mandibular invasion in squamous cell carcinoma of the oral cavity and oropharynx. Eur Ann Otorhinolaryngol Head Neck Dis 136:361–366. https://doi.org/10.1016/janorl201905005

Buller J, Borggrefe J, Zirk M, Benzer E, Zöller JE, Dreiseidler T (2020) Does SPECT provide incremental value to CT or MRI in assessing mandibular invasion by oral squamous cell carcinoma? J Oral Maxillofac Surg 78:835–843. https://doi.org/10.1016/j.joms.2019.12.011

DeAngelis A, Breik O, Angel C, Goh C, Iseli T, Nastri A et al (2019) Can radiological examination of mandibular bone invasion accurately predict the need for mandibular resection in oral squamous cell carcinoma? Int J Oral Maxillofac Surg 48:576–583. https://doi.org/10.1016/j.ijom.2018.12.007

Farrow ES, Boulanger T, Wojcik T, Lemaire AS, Raoul G, Julieron M (2016) Magnetic resonance imaging and computed tomography in the assessment of mandibular invasion by squamous cell carcinoma of the oral cavity. Influence on surgical management and post-operative course. Rev Stomatol Chir Maxillofac Chir Orale 117:311–321. https://doi.org/10.1016/j.revsto.2016.06.004

Hayashi K, Kikuchi M, Imai Y, Yamashita D, Hino M, Ito K et al (2020) Clinical value of fused PET/MRI for surgical planning in patients with oral/oropharyngeal carcinoma. Laryngoscope 130:367–374. https://doi.org/10.1002/lary.27911

Huang SH, Chien CY, Lin WC, Fang FM, Wang PW, Lui CC et al (2011) A comparative study of fused FDG PET/MRI, PET/CT, MRI, and CT imaging for assessing surrounding tissue invasion of advanced buccal squamous cell carcinoma. Clin Nuclear Med 36:518–525. https://doi.org/10.1097/RLU.0b013e318217566f

Imaizumi A, Yoshino N, Yamada I, Nagumo K, Amagasa T, Omura K et al (2006) A potential pitfall of MR imaging for assessing mandibular invasion of squamous cell carcinoma in the oral cavity. AJNR Am J Neuroradiol 27:114–122

CAS   PubMed   PubMed Central   Google Scholar  

Kim M, Higuchi T, Arisaka Y, Achmad A, Tokue A, Tominaga H et al (2013) Clinical significance of 18 F-α-methyl tyrosine PET/CT for the detection of bone marrow invasion in patients with oral squamous cell carcinoma: comparison with 18 F-FDG PET/CT and MRI. Ann Nuclear Med 27:423–430. https://doi.org/10.1007/s12149-013-0701-0

Article   CAS   Google Scholar  

Kouketsu A, Miyashita H, Kojima I, Sakamoto M, Murata T, Mori S et al (2021) Comparison of different diagnostic imaging techniques for the detection of bone invasion in oral cancers. Oral Oncol 120:105453. https://doi.org/10.1016/j.oraloncology.2021.105453

Linz C, Müller-Richter UD, Buck AK, Mottok A, Ritter C, Schneider P et al (2015) Performance of cone beam computed tomography in comparison to conventional imaging techniques for the detection of bone invasion in oral cancer. Int J Oral Maxillofac Surg 44:8–15. https://doi.org/10.1016/j.ijom.2014.07.023

Nae A, O’Leary G, Feeley L, Fives C, Fitzgerald B, Chiriac E et al (2019) Utility of CT and MRI in assessment of mandibular involvement in oral cavity cancer. World J Otorhinolaryngol Head Neck Surg 5:71–75. https://doi.org/10.1016/j.wjorl.2019.02.001

Rajesh A, Khan A, Kendall C, Hayter J, Cherryman G (2008) Can magnetic resonance imaging replace single photon computed tomography and computed tomography in detecting bony invasion in patients with oral squamous cell carcinoma?. Br J Oral Maxillofac Surg 46:11–14. https://doi.org/10.1016/j.bjoms.2007.08.024

Silva M, Zambrini EI, Chiari G, Montermini I, Manna C, Poli T et al (2016) Pre-surgical assessment of mandibular bone invasion from oral cancer: comparison between different imaging techniques and relevance of radiologist expertise. Radiol Med 121:704–710. https://doi.org/10.1007/s11547-016-0654-1

Smyth DA, O’Dwyer TP, Keane CO, Stack J (1996) Predicting mandibular invasion in mouth cancer. Clin Otolaryngol Allied Sci 21:265–268. https://doi.org/10.1111/j1365-22731996tb01738x

Suzuki N, Kuribayashi A, Sakamoto K, Sakamoto J, Nakamura S, Watanabe H et al (2019) Diagnostic abilities of 3T MRI for assessing mandibular invasion of squamous cell carcinoma in the oral cavity: comparison with 64-row multidetector CT. Dentomaxillofac Radiol 48:20180311. https://doi.org/10.1259/dmfr.20180311

Tsue TT, McCulloch TM, Girod DA, Couper DJ, Weymuller EA Jr., Glenn MG (1994) Predictors of carcinomatous invasion of the mandible. Head Neck 16:116–126. https://doi.org/10.1002/hed.2880160204

van den Brekel MW, Runne RW, Smeele LE, Tiwari RM, Snow GB, Castelijns JA (1998) Assessment of tumour invasion into the mandible: the value of different imaging techniques. Eur Radiol 8:1552–1557. https://doi.org/10.1007/s003300050585

Vidiri A, Guerrisi A, Pellini R, Manciocco V, Covello R, Mattioni O et al (2010) Multi-detector row computed tomography (MDCT) and magnetic resonance imaging (MRI) in the evaluation of the mandibular invasion by squamous cell carcinomas (SCC) of the oral cavity. Correlation with pathological data. J Exp Clin Cancer Res 29:73. https://doi.org/10.1186/1756-9966-29-73

Wiener E, Pautke C, Link TM, Neff A, Kolk A (2006) Comparison of 16-slice MSCT and MRI in the assessment of squamous cell carcinoma of the oral cavity. Eur J Radiol 58:113–118. https://doi.org/10.1016/jejrad200511006

Zupi A, Califano L, Maremonti P, Longo F, Ciccarelli R, Soricelli A (1996) Accuracy in the diagnosis of mandibular involvement by oral cancer. J Craniomaxillofac Surg 24:281–284. https://doi.org/10.1016/s1010-5182(96)80059-x

Brown JS, Crowder LP, Willcocks EJ, Bajwa MS, Schache AG (2023) Accuracy of preoperative clinical examination and imaging for mandibular resection in oral cancer: a systematic review of diagnostic test accuracy studies. Br J Oral Maxillofac Surg 61:28–38. https://doi.org/10.1016/j.bjoms.2022.10.005

Cao C, Gan X, He Y, Su Y, Liu Z, Hu X et al (2021) Diagnostic efficacy of PET-CT, CT, and MRI in preoperative assessment of mandibular invasion caused by head and neck cancer: a systematic review and meta-analysis. Oral Oncol 116:105264. https://doi.org/10.1016/j.oraloncology.2021.105264

Klenk C, Gawande R, Uslu L, Khurana A, Qiu D, Quon A et al (2014) Ionising radiation-free whole-body MRI versus (18)F-fluorodeoxyglucose PET/CT scans for children and young adults with cancer: a prospective, non-randomised, single-centre study. Lancet Oncol 15:275–285. https://doi.org/10.1016/s1470-2045(14)70021-x

Zaitsev M, Maclaren J, Herbst M (2015) Motion artifacts in MRI: a complex problem with many partial solutions. J Magn Reson Imaging 42:887–901. https://doi.org/10.1002/jmri.24850

Giantsoudi D, De Man B, Verburg J, Trofimov A, Jin Y, Wang G et al (2017) Metal artifacts in computed tomography for radiation therapy planning: dosimetric effects and impact of metal artifact reduction. Phys Med Biol 62:R49. https://doi.org/10.1088/1361-6560/aa5293

Murtojärvi S, Malaspina S, Kinnunen I, Tuokkola T, Honka MJ, Saunavaara V et al (2022) Diagnostic accuracy of (18)F-FDG-PET/CT and (18)F-FDG-PET/MRI in detecting locoregional recurrence of HNSCC 12 weeks after the end of chemoradiotherapy: single-center experience with PET/MRI. Contrast Media Mol Imaging. https://doi.org/10.1155/2022/8676787

Nensa F, Beiderwellen K, Heusch P, Wetter A (2014) Clinical applications of PET/MRI: current status and future perspectives. Diagn Interv Radiol 20:438–447. https://doi.org/10.5152/dir.2014.14008

Singnurkar A, Poon R, Metser U (2024) Head-to-head comparison of the diagnostic performance of FDG PET/CT and FDG PET/MR in patients with cancer: a systematic review and meta-analysis. AJR Am J Roentgenol. https://doi.org/10.2214/ajr.24.31519

Download references

The article was funded by the Science and Technology Project of Guangzhou (Grant Numbers 202002030095).

Author information

Authors and affiliations.

School of Stomatology, Southern Medical University, Guangzhou, China

Department of Stomatology, General Hospital of Southern Theater Command, Guangzhou, China

You can also search for this author in PubMed   Google Scholar

Contributions

XL: Data Collection and Literature Search SQZ: Literature Search and Review, Manuscript Writing, Meta-Analysis, Content Planning.

Corresponding author

Correspondence to Xiao Li .

Ethics declarations

Conflict of interest.

The authors declare no commercial or financial relationships that could be perceived as potential conflicts of interest in the conduct of this research.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Zhao, S., Li, X. The diagnosis performance of [ 18 F]FDG PET/CT, MRI, and CT in the diagnosis of mandibular invasion in oral/oropharyngeal carcinoma: a head-to-head comparative meta-analysis. Clin Transl Imaging (2024). https://doi.org/10.1007/s40336-024-00657-w

Download citation

Received : 17 June 2024

Accepted : 14 August 2024

Published : 26 August 2024

DOI : https://doi.org/10.1007/s40336-024-00657-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• [ 18 F]FDG PET/CT
• Mandibular invasion
• Oral/oropharyngeal carcinoma
• Meta-analysis
• Find a journal
• Publish with us
• Track your research