Big Data in ehealthcare: Challenges and Perspectives

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 EHR
		2.5.3 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 Considerations
		3.8.2 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 ANN
		5.2.2 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 Mining
	5.6 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
		6.3.6 Recent Advances in Computer Architecture
	6.4 Exploiting Parallelism
	6.5 MapReduce Overview
		6.5.1 MapReduce Programming Model
		6.5.2 MapReduce Framework Implementation
	6.6 Hadoop
		6.6.1 Hadoop Architecture
		6.6.2 Resource Provisioning Framework
		6.6.3 Hadoop Distributed File System
		6.6.4 MapReduce Framework
		6.6.5 Hadoop Common
		6.6.6 Hadoop Performance
	6.7 Hadoop Ecosystem
		6.7.1 Apache Spark
		6.7.2 Apache ZooKeeper
	6.8 Streaming Data Processing
		6.8.1 Apache Flume
		6.8.2 Spark Streaming
		6.8.3 Amazon Kinesis Streaming Data Platform
	6.9 Chapter Notes
	References
7: Big Data Storage
	7.1 Introduction
	7.2 Structured vs. Unstructured Data
	7.3 Problems with Relational Databases
	7.4 NoSQL Databases
	7.5 Document-oriented Databases
		7.5.1 MongoDB
		7.5.2 Apache CouchDB
	7.6 Column-oriented Databases
		7.6.1 Apache Cassandra
		7.6.2 Apache HBase
	7.7 Graph Databases
		7.7.1 Neo4j
	7.8 Health Data Storage: A Case Study
	7.9 Chapter Notes
	References
8: Security and Privacy for Health Data
	8.1 Introduction
		8.1.1 Security
		8.1.2 Privacy
		8.1.3 Privacy-Preserving Data Management
		8.1.4 A Few Common Questions
	8.2 Security and Privacy Issues
		8.2.1 Storage
		8.2.2 Security Breach
		8.2.3 Data Mingling
		8.2.4 Data Sensitivity
		8.2.5 User
		8.2.6 Computations
		8.2.7 Transaction Logs
		8.2.8 Validation of Input
		8.2.9 Data Mining
		8.2.10 Access Control
		8.2.11 Data Audit
		8.2.12 Data Source
		8.2.13 Security Best Practices
		8.2.14 Software Security
		8.2.15 Secure Hardware
		8.2.16 User Account Management
		8.2.17 Clustering and Auditing of Databases
	8.3 Challenges
		8.3.1 Malicious User
		8.3.2 Identifying Threats
		8.3.3 Risk Mitigation
		8.3.4 Real-Time Monitoring
		8.3.5 Privacy Preservation
			8.3.5.1 Health Data Sale
			8.3.5.2 Compliance for EHR
	8.4 Security of NoSQL Databases
		8.4.1 Reviewing Security in NoSQL databases
			8.4.1.1 Security in Cassandra
			8.4.1.2 Security in MongoDB
			8.4.1.3 Security in HBase
		8.4.2 Reviewing Enterprise Approaches towards Security in NoSQL Databases
	8.5 Integrating Security with Big Data Solutions
		8.5.1 Big Data Enterprise Security
	8.6 Secured Health Data Delivery: A Case Study
	8.7 Chapter Notes
	References
Index