Big Data in ehealthcare: Challenges and Perspectives

256 Pages·2019·22.508 MB·English

by  Nandini Mukherjee, Sarmistha Neogy, Samiran Chattopadhyay

Big Data in eHealthcare
Challenges and Perspectives

Big Data in eHealthcare
Challenges and Perspectives
Nandini Mukherjee
Sarmistha Neogy
Samiran Chattopadhyay
CRC Press
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Library of Congress Cataloging-in-Publication Data
Names: Mukherjee, Nandini, author. | Neogy, Sarmistha, author. |
Chattopadhyay, Samiran, author.
Title: Big data in eHealthcare : challenges and perspectives / Nandini
Mukherjee, Sarmistha Neogy, Samiran Chattopadhyay.
Description: Boca Raton, FL : CRC Press/Taylor & Francis Group, 2019. |
Includes bibliographical references and index.
Identifiers: LCCN 2019011925| ISBN 9780815394402 (hardback : acid-free paper)
| ISBN 9781351057790 (ebook)
Subjects: LCSH: Medical informatics. | Medicine--Data processing. | Big data.
| Data mining.
Classification: LCC R858 .M85 2019 | DDC 610.285--dc23
LC record available at https://lccn.loc.gov/2019011925
Visit the Taylor & Francis Web site at
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To my daughter Shabnam and my husband Biman
— Nandini Mukherjee
To my daughter Roshni and my son-in-law Prince Bose
— Sarmistha Neogy
To my daughter Anwesha and my wife Matangini
— Samiran Chattopadhyay

Contents
List of Figures xiii
Preface xv
Acknowledgements xvii
Authors xix
1 Introduction 1
1.1 What Is eHealth? . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 eHealth Technologies . . . . . . . . . . . . . . . . . . . . . . 3
1.3 eHealth Applications . . . . . . . . . . . . . . . . . . . . . . 4
1.3.1 Health Informatics . . . . . . . . . . . . . . . . . . . . 5
1.3.2 mHealth . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3.3 Telehealth . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 eHealth and Big Data . . . . . . . . . . . . . . . . . . . . . . 8
1.5 Issues and Challenges . . . . . . . . . . . . . . . . . . . . . . 9
1.6 Chapter Notes . . . . . . . . . . . . . . . . . . . . . . . . . . 11
References 12
2 Electronic Health Records 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Electronic Health Records . . . . . . . . . . . . . . . . . . . 14
2.3 EHR Standards . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.1 ISO 13606 . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.2 HL7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.3 OpenEHR . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.4 Adoption of EHR Standards . . . . . . . . . . . . . . . . . . 30
2.5 Ontology-based Approaches . . . . . . . . . . . . . . . . . . . 32
2.5.1 Developing an Ontology . . . . . . . . . . . . . . . . . 33
2.5.2 Ontologies for EHR . . . . . . . . . . . . . . . . . . . 33
2.5.3 Ontologies in Healthcare . . . . . . . . . . . . . . . . . 34
2.6 Chapter Notes . . . . . . . . . . . . . . . . . . . . . . . . . . 35
vii
viii Contents
References 40
3 Big Data: From Hype to Action 43
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2 What Is Big Data? . . . . . . . . . . . . . . . . . . . . . . . 44
3.3 Big Data Properties . . . . . . . . . . . . . . . . . . . . . . . 45
3.4 Why Is Big Data Important? . . . . . . . . . . . . . . . . . . 47
3.5 Big Data in the World . . . . . . . . . . . . . . . . . . . . . . 49
3.6 Big Data in Healthcare . . . . . . . . . . . . . . . . . . . . . 50
3.6.1 Is Health Data Big Data? . . . . . . . . . . . . . . . . 51
3.6.2 Big Data: Healthcare Providers . . . . . . . . . . . . . 52
3.7 Other Big Data Applications . . . . . . . . . . . . . . . . . . 54
3.7.1 Banking and Securities. . . . . . . . . . . . . . . . . . 55
3.7.2 Communications, Media, and Entertainment . . . . . 55
3.7.3 Manufacturing and Natural Resources . . . . . . . . . 56
3.7.4 Government . . . . . . . . . . . . . . . . . . . . . . . . 56
3.7.5 Transportation . . . . . . . . . . . . . . . . . . . . . . 56
3.7.6 Education . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.8 Securing Big Data . . . . . . . . . . . . . . . . . . . . . . . . 57
3.8.1 Security Considerations . . . . . . . . . . . . . . . . . 57
3.8.2 Security Requirements . . . . . . . . . . . . . . . . . . 58
3.8.3 Some Observations . . . . . . . . . . . . . . . . . . . . 59
3.9 Big Data Security Framework . . . . . . . . . . . . . . . . . 59
3.10 Chapter Notes . . . . . . . . . . . . . . . . . . . . . . . . . . 61
References 62
4 Acquisition of Big Health Data 65
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.2 Wireless Body Area Network . . . . . . . . . . . . . . . . . . 66
4.2.1 BAN Design Aspects . . . . . . . . . . . . . . . . . . . 68
4.2.2 WBAN Sensors . . . . . . . . . . . . . . . . . . . . . . 70
4.2.3 Technologies for WBAN . . . . . . . . . . . . . . . . . 70
4.2.3.1 Bluetooth and Bluetooth LE . . . . . . . . . 70
4.2.3.2 ZigBee and WLAN . . . . . . . . . . . . . . 71
4.2.3.3 WBAN standard . . . . . . . . . . . . . . . . 71
4.2.4 Network Layer . . . . . . . . . . . . . . . . . . . . . . 73
4.2.5 Inter-WBAN Interference . . . . . . . . . . . . . . . . 74
4.3 Crowdsourcing . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.4 Social Network . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.5 Chapter Notes . . . . . . . . . . . . . . . . . . . . . . . . . . 83
References 84
Contents ix
5 Health Data Analytics 87
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.2 Artificial Neural Networks . . . . . . . . . . . . . . . . . . . 90
5.2.1 Model of an ANN . . . . . . . . . . . . . . . . . . . . 90
5.2.2 Modes of ANN . . . . . . . . . . . . . . . . . . . . . . 92
5.2.3 Structure of ANNs . . . . . . . . . . . . . . . . . . . . 93
5.2.4 Training a Feedforward Neural Network . . . . . . . . 93
5.2.5 ANN in Medical Domain . . . . . . . . . . . . . . . . 96
5.2.6 Weakness of ANNs . . . . . . . . . . . . . . . . . . . . 97
5.3 Classification and Clustering . . . . . . . . . . . . . . . . . . 98
5.3.1 Clustering via K-Means . . . . . . . . . . . . . . . . . 99
5.3.2 Some Additional Remarks about K-Means . . . . . . . 100
5.4 Statistical Classifier: Bayesian and Naive Classification . . . 102
5.4.1 Experiments with Medical Data . . . . . . . . . . . . 104
5.4.2 Decision Trees . . . . . . . . . . . . . . . . . . . . . . 106
5.4.3 Clasical Indction of Decision Trees . . . . . . . . . . . 108
5.5 Association Rule Mining (ARM) . . . . . . . . . . . . . . . . 111
5.5.1 Simple Approach for Rule Discovery . . . . . . . . . . 112
5.5.2 Processing of Medical Data . . . . . . . . . . . . . . . 113
5.5.3 Association Rule Mining in Health Data . . . . . . . . 113
5.5.4 Issues with Association Rule Mining . . . . . . . . . . 114
5.6 Time Series Analysis . . . . . . . . . . . . . . . . . . . . . . 115
5.6.1 Time Series Regression Models . . . . . . . . . . . . . 116
5.6.2 Linear AR Time Series Models . . . . . . . . . . . . . 117
5.6.3 Application of Time Series . . . . . . . . . . . . . . . 120
5.7 Text Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.7.1 Term Frequency and Inverse Document Frequency . . 122
5.7.2 Topic Modeling . . . . . . . . . . . . . . . . . . . . . . 123
5.8 Chapter Notes . . . . . . . . . . . . . . . . . . . . . . . . . . 124
References 126
6 Architecture and Computational Models for Big Data Pro-
cessing 129
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
6.2 Performance Issues . . . . . . . . . . . . . . . . . . . . . . . 130
6.3 Parallel Architecture . . . . . . . . . . . . . . . . . . . . . . 135
6.3.1 Distributed Shared Memory . . . . . . . . . . . . . . . 137
6.3.2 Hierarchical Hybrid Architecture . . . . . . . . . . . . 138
6.3.3 Cluster Computing . . . . . . . . . . . . . . . . . . . . 138
6.3.4 Multicore Architecture . . . . . . . . . . . . . . . . . . 139
6.3.5 GPU Computing . . . . . . . . . . . . . . . . . . . . . 140
6.3.6 Recent Advances in Computer Architecture . . . . . . 141