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Big Data in EHealth : Challenges and Perspectives.

Author: Nandini Mukherjee; Sarmistha Neogy; Samiran Chattopadhyay
Publisher: Milton : Chapman and Hall/CRC, 2019.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Mukherjee, Nandini.
Big Data in EHealth : Challenges and Perspectives.
Milton : Chapman and Hall/CRC, ©2019
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Nandini Mukherjee; Sarmistha Neogy; Samiran Chattopadhyay
ISBN: 9781351057783 1351057782 9781351057776 1351057774 9781351057769 1351057766 9781351057790 1351057790
OCLC Number: 1083469510
Notes: 6.3.6 Recent Advances in Computer Architecture
Description: 1 online resource (256 pages)
Contents: Cover; Half Title; Title Page; Copyright Page; Dedication; Table of Contents; List of Figures; Preface; Acknowledgements; Authors; 1: Introduction; 1.1 What Is eHealth?; 1.2 eHealth Technologies; 1.3 eHealth Applications; 1.3.1 Health Informatics; 1.3.2 mHealth; 1.3.3 Telehealth; 1.4 eHealth and Big Data; 1.5 Issues and Challenges; 1.6 Chapter Notes; References; 2: Electronic Health Records; 2.1 Introduction; 2.2 Electronic Health Records; 2.3 EHR Standards; 2.3.1 ISO 13606; 2.3.2 HL7; 2.3.3 OpenEHR; 2.4 Adoption of EHR Standards; 2.5 Ontology-based Approaches; 2.5.1 Developing an Ontology 2.5.2 Ontologies for EHR2.5.3 Ontologies in Healthcare; 2.6 Chapter Notes; References; 3: Big Data: From Hype to Action; 3.1 Introduction; 3.2 What Is Big Data?; 3.3 Big Data Properties; 3.4 Why Is Big Data Important?; 3.5 Big Data in the World; 3.6 Big Data in Healthcare; 3.6.1 Is Health Data Big Data?; 3.6.2 Big Data: Healthcare Providers; 3.7 Other Big Data Applications; 3.7.1 Banking and Securities; 3.7.2 Communications, Media, and Entertainment; 3.7.3 Manufacturing and Natural Resources; 3.7.4 Government; 3.7.5 Transportation; 3.7.6 Education; 3.8 Securing Big Data 3.8.1 Security Considerations3.8.2 Security Requirements; 3.8.3 Some Observations; 3.9 Big Data Security Framework; 3.10 Chapter Notes; References; 4: Acquisition of Big Health Data; 4.1 Introduction; 4.2 Wireless Body Area Network; 4.2.1 BAN Design Aspects; 4.2.2 WBAN Sensors; 4.2.3 Technologies for WBAN; 4.2.3.1 Bluetooth and Bluetooth LE; 4.2.3.2 ZigBee and WLAN; 4.2.3.3 WBAN standard; 4.2.4 Network Layer; 4.2.5 Inter-WBAN Interference; 4.3 Crowdsourcing; 4.4 Social Network; 4.5 Chapter Notes; References; 5: Health Data Analytics; 5.1 Introduction; 5.2 Articial Neural Networks 5.2.1 Model of an ANN5.2.2 Modes of ANN; 5.2.3 Structure of ANNs; 5.2.4 Training a Feedforward Neural Network; 5.2.5 ANN in Medical Domain; 5.2.6 Weakness of ANNs; 5.3 Classication and Clustering; 5.3.1 Clustering via K-Means; 5.3.2 Some Additional Remarks about K-Means; 5.4 Statistical Classifier: Bayesian and Naive Classification; 5.4.1 Experiments with Medical Data; 5.4.2 Decision Trees; 5.4.3 Clasical Indction of Decision Trees; 5.5 Association Rule Mining (ARM); 5.5.1 Simple Approach for Rule Discovery; 5.5.2 Processing of Medical Data; 5.5.3 Association Rule Mining in Health Data 5.5.4 Issues with Association Rule Mining5.6 Time Series Analysis; 5.6.1 Time Series Regression Models; 5.6.2 Linear AR Time Series Models; 5.6.3 Application of Time Series; 5.7 Text Mining; 5.7.1 Term Frequency and Inverse Document Frequency; 5.7.2 Topic Modeling; 5.8 Chapter Notes; References; 6: Architecture and Computational Models for Big Data Processing; 6.1 Introduction; 6.2 Performance Issues; 6.3 Parallel Architecture; 6.3.1 Distributed Shared Memory; 6.3.2 Hierarchical Hybrid Architecture; 6.3.3 Cluster Computing; 6.3.4 Multicore Architecture; 6.3.5 GPU Computing

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