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HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS
The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners.
The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information.
A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare.
In addition, the reader will find in this Handbook:
* Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning;
* An exploration of predictive analytics in healthcare;
* The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics.
Audience
Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.
List of contents
Preface xvii
1 An Introduction to Knowledge Engineering and Data Analytics 1
D. Karthika and K. Kalaiselvi
1.1 Introduction 2
1.1.1 Online Learning and Fragmented Learning Modeling 2
1.2 Knowledge and Knowledge Engineering 5
1.2.1 Knowledge 5
1.2.2 Knowledge Engineering 5
1.3 Knowledge Engineering as a Modelling Process 6
1.4 Tools 7
1.5 What are KBSs? 8
1.5.1 What is KBE? 8
1.5.2 When Can KBE Be Used? 10
1.5.3 CAD or KBE? 12
1.6 Guided Random Search and Network Techniques 13
1.6.1 Guide Random Search Techniques 13
1.7 Genetic Algorithms 14
1.7.1 Design Point Data Structure 15
1.7.2 Fitness Function 15
1.7.3 Constraints 16
1.7.4 Hybrid Algorithms 16
1.7.5 Considerations When Using a GA 16
1.7.6 Alternative to Genetic-Inspired Creation of Children 17
1.7.7 Alternatives to GA 18
1.7.8 Closing Remarks for GA 18
1.8 Artificial Neural Networks 19
1.9 Conclusion 19
References 20
2 A Framework for Big Data Knowledge Engineering 21
Devi T. and Ramachandran A.
2.1 Introduction 22
2.1.1 Knowledge Engineering in AI and Its Techniques 23
2.1.1.1 Supervised Model 23
2.1.1.2 Unsupervised Model 23
2.1.1.3 Deep Learning 24
2.1.1.4 Deep Reinforcement Learning 24
2.1.1.5 Optimization 25
2.1.2 Disaster Management 25
2.2 Big Data in Knowledge Engineering 26
2.2.1 Cognitive Tasks for Time Series Sequential Data 27
2.2.2 Neural Network for Analyzing the Weather Forecasting 27
2.2.3 Improved Bayesian Hidden Markov Frameworks 28
2.3 Proposed System 30
2.4 Results and Discussion 32
2.5 Conclusion 33
References 36
3 Big Data Knowledge System in Healthcare 39
P. Sujatha, K. Mahalakshmi and P. Sripriya
3.1 Introduction 40
3.2 Overview of Big Data 41
3.2.1 Big Data: Definition 41
3.2.2 Big Data: Characteristics 42
3.3 Big Data Tools and Techniques 43
3.3.1 Big Data Value Chain 43
3.3.2 Big Data Tools and Techniques 45
3.4 Big Data Knowledge System in Healthcare 45
3.4.1 Sources of Medical Big Data 51
3.4.2 Knowledge in Healthcare 53
3.4.3 Big Data Knowledge Management Systems in Healthcare 55
3.4.4 Big Data Analytics in Healthcare 56
3.5 Big Data Applications in the Healthcare Sector 59
3.5.1 Real Time Healthcare Monitoring and Altering 59
3.5.2 Early Disease Prediction with Big Data 59
3.5.3 Patients Predictions for Improved Staffing 61
3.5.4 Medical Imaging 61
3.6 Challenges with Healthcare Big Data 62
3.6.1 Challenges of Big Data 62
3.6.2 Challenges of Healthcare Big Data 62
3.7 Conclusion 64
References 64
4 Big Data for Personalized Healthcare 67
Dhanalakshmi R. and Jose Anand
4.1 Introduction 68
4.1.1 Objectives 68
4.1.2 Motivation 69
4.1.3 Domain Description 70
4.1.4 Organization of the Chapter 70
4.2 Related Literature 71
4.2.1 Healthcare Cyber Physical System Architecture 71
4.2.2 Healthcare Cloud Architecture 71
4.2.3 User Authentication Management 72
4.2.4 Healthcare as a Service (HaaS) 72
4.2.5 Reporting Services 73
4.2.6 Chart and Trend Analysis 73
4.2.7 Medical Data Analysis 73
4.2.8 Hospital Platf
About the author
A. Jaya, PhD, Professor in the Department of Computer Applications, B. S. Abdur Rahman Crescent Institute of Science and Technology, India. She has published more than 90 research articles in international journals.
K. Kalaiselvi, PhD, is a Professor and Head in the Department of Computer Science, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies, Chennai, India. She has published more than 50 research articles in international journals. Dinesh Goyal, PhD, is Principal at the Poornima Institute of Engineering & Technology, Jaipur, India. He has six patents published as well as six books and numerous articles. Dhiya Al-Jumeily, PhD, is a professor of Artificial Intelligence and the Associate Dean of External Engagement for the Faculty of Engineering and Technology, Liverpool John Moores University, UK. He has published well over 200 peer-reviewed scientific publications, six books, and five book chapters. His current research is on decision support systems for self-management of health and disease.
Summary
HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS
The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners.
The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information.
A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare.
In addition, the reader will find in this Handbook:
* Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning;
* An exploration of predictive analytics in healthcare;
* The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics.
Audience
Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.
