__Big Data Analytics for Intelligent Healthcare Management__ covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data.

lgrsnf/Big Data Analytics for Intelligent Healthcare Management.pdf

zlib/Self-Help, Relationships & Lifestyle/Health - Diseases & Disorders/Nilanjan Dey/Big Data Analytics for Intelligent Healthcare Management_5511898.pdf

Nilanjan Dey; Himansu Das; Bighnaraj Naik; Himansu Sekhar Behera

Elsevier Science & Technology

Academic Press, Incorporated

Morgan Kaufmann Publishers

Brooks/Cole

Advances in ubiquitous sensing applications for healthcare, volume three, London, 2019

United States, United States of America

Elsevier Ltd., London, 2019

1, FR, 2019

2013

lg2522866

{"edition":"1","isbns":["012818146X","9780128181461"],"last_page":312,"publisher":"Academic Press","series":"Advances in ubiquitous sensing applications for healthcare"}

Cover
Big Data Analytics for
Intelligent Healthcare
Management
Copyright
Contributors
Preface
Acknowledgments
1
Bio-Inspired Algorithms for Big Data Analytics: A Survey, Taxonomy, and Open Challenges
Introduction
Dimensions of Data Management
Big Data Analytical Model
Bio-Inspired Algorithms for Big Data Analytics: A Taxonomy
Evolutionary Algorithms
Swarm-Based Algorithms
Ecological Algorithms
Discussions
Future Research Directions and Open Challenges
Resource Scheduling and Usability
Data Processing and Elasticity
Resilience and Heterogeneity in Interconnected Clouds
Sustainability and Energy-Efficiency
Data Security and Privacy Protection
IoT-Based Edge Computing and Networking
Emerging Research Areas in Bio-Inspired Algorithm-Based Big Data Analytics
Container as a Service (CaaS)
Serverless Computing as a Service (SCaaS)
Blockchain as a Service (BaaS)
Software-defined Cloud as a Service (SCaaS)
Deep Learning as a Service (DLaaS)
Bitcoin as a Service (BiaaS)
Quantum Computing as a Service (QCaaS)
Summary and Conclusions
Acknowledgments
References
Further Reading
2
Big Data Analytics Challenges and Solutions
Introduction
Consumable Massive Facts Analytics
Allotted Records Mining Algorithms
Gadget Failure
Facts Aggregation Challenges
Statistics Preservation-Demanding Situations
Information Integration Challenges
Records Analysis Challenges
Scale of the Statistics
Pattern Interpretation Challenges
Arrangements of Challenges
User Intervention Method
Probabilistic Method
Defining and Detecting Anomalies in Human Ecosystems
Demanding Situations in Managing Huge Records
Massive Facts Equal Large Possibilities
Present Answers to Challenges for the Quantity Mission
Hadoop
Hadoop-distributed file system
Hadoop MapReduce
Apache spark
Grid computing
Spark structures
Capacity solutions for records-variety trouble
Image Mining and Processing With Big Data
Potential Answers for Velocity Trouble
Transactional databases
Statistics representation
Massive actualities calculations
Ability solutions for privateers and safety undertaking
Ability Solutions for Scalability Assignments
Big data and cloud computing
Cloud computing service models
Answers
Use record encryption
Imposing access controls
Logging
Discussion
Conclusion
Glossary
References
Further Reading
3
Big Data Analytics in Healthcare: A Critical Analysis
Introduction
Big Data
Healthcare Data
Structured Data
Unstructured Data
Semistructured Data
Genomic Data
Patient Behavior and Sentiment Data
Clinical Data and Clinical Notes
Clinical Reference and Health Publication Data
Administrative and External Data
Medical Image Processing and its Role in Healthcare Data Analysis
Recent Works in Big Data Analytics in Healthcare Data
Architectural Framework and Different Tools for Big Data Analytics in Healthcare Big Data
Architectural Framework
Different Tools Used in Big Data Analytics in Healthcare Data
Challenges Faced During Big Data Analytics in Healthcare
Conclusion and Future Research
References
Further Reading
4
Transfer Learning and Supervised Classifier Based Prediction Model for Breast Cancer
Introduction
Related Work
Dataset and Methodologies
Convolution Neural Networks (CNNs/ConvNets)
Transfer learning and convolution networks
Convolution networks as fixed feature extractors
Dimensionality reduction and principle component analysis (PCA)
Supervised machine learning
Proposed Model
Implementation
Feature Extraction
Dimensionality Reduction
Classification
Tuning Hyperparameters of the Classifiers
Result and Analysis
10-fold Cross Validation Result
Magnification Factor Wise Analysis on Validation Accuracy
Validation accuracy of 40x
Validation accuracy of 100x
Validation accuracy of 200x
Validation accuracy of 400x
Best validation accuracy
Performance on the test set
Result and Analysis of Test Performance
Test performance on 40x
Overall performance on 40x
Test performance on 100x
Overall performance on 100x
Test performance on 200x
Test performance on 400x
Overall performance on 400x
Discussion
Conclusion
References
Further Reading
5
Chronic TTH Analysis by EMG and GSR Biofeedback on Various Modes and Various Medical Symptoms Using IoT
Introduction and Background
Biofeedback
Mental Health Introduction
Importance of Mental Health, Stress, and Emotional Needs and Significance of Study
Meaning of Mental Health
Definitions
Factors Affecting Mental Health
Models of Stress: Three Models in Practice
Types of stress
Causes of stress
Symptoms of stress
Big Data and IoT
Previous Studies (Literature Review)
Tension Type Headache and Stress
Independent Variable: Emotional Need Fulfillment
Meditation-Effective Spiritual Tool With Approach of Biofeedback EEG
Mind-Body and Consciousness
Sensor Modalities and Our Approach
Biofeedback Based Sensor Modalities
Electromyograph
Electrodermograph
Proposed Framework
Experiments and Results-Study Plot
Study Design and Source of Data
Study Duration and Consent From Subjects
Sampling Design and Allocation Process
Sample Size
Study Population
Inclusion criteria
Exclusion criteria
Intervention
Outcome Parameters
Primary variables
Secondary variables
Analgesic Consumption
Assessment of Outcome Variables
Pain Diary
Data Collection
Statistical Analysis
Hypothesis
Data Collection Procedure-Guided Meditation as per Fig. 5.7G
Results, Interpretation and Discussion
The Trend of Average of Frequency
The Trend of Average of Duration
The Trend of Average of Intensity
The Trend of Duration per Cycle With Time
Trend on Correlation of TTH Duration and Intensity
Trend on Correlation of TTH Duration With Occurrence
The Trend of Average of Frequency
The Trend of Average of Duration
The Trend of Average of Intensity
The Trend of Duration per Cycle With Time
Trend on Correlation of TTH Duration and Intensity
Trend on Correlation of TTH Duration With Occurrence
The Trend of Average of Frequency
The Trend of Average Duration
The Trend of Average Intensity
The Trend of Duration per Cycle With Time
Trend on Correlation of TTH Duration and Intensity
Trend on Correlation of TTH Duration With Occurrence
The Trend of Average of Frequency
The Trend of Average of Duration
The Trend of Average Intensity
The Trend of Duration per Cycle With Time
Trend on Correlation of TTH Duration and Intensity
Trend on Correlation of TTH Duration With Occurrence
Findings in This Chapter
Future Scope, Limitations, and Possible Applications
Conclusion
Comprehensive Conclusion
Acknowledgment
References
Further Reading
6
Multilevel Classification Framework of fMRI Data: A Big Data Approach
Introduction
Related Work
Our Approach
Dataset
Methodology
Result Evaluation
Experimental Results
Subject-Dependent Experiments on PS+SP
All features
ROI-based feature
Average ROI-based feature
N-most active-based feature
N-most active ROI-based feature
Subject-Dependent Experiment on PS/SP
ROI-based feature
Average ROI-based feature
N-most active-based feature
Most active ROI-based feature
Result Analysis
Summary of the Subject-Dependent Results
Subject-Independent Experiment
Conclusion and Future Work
References
Further Reading
7
Smart Healthcare: An Approach for Ubiquitous Healthcare Management Using IoT
Introduction
Literature Survey
Proposed Model
Fetch Module
Ingest Module
Retrieve Module
Act/Notify Module
Prototype Model of the Proposed Work
Implementation of the Proposed System
Simulation and Result Discussion
Conclusion
References
8
Blockchain in Healthcare: Challenges and Solutions
Introduction
Roadmap
Healthcare Big Data and Blockchain Overview
Healthcare Big Data
Blockchain
How Blockchain Works
Privacy of Healthcare Big Data
Privacy Right by Country and Organization
How Blockchain Is Applicable for Healthcare Big Data
Digital Trust
Intelligent Data Management
Smart Ecosystem
Digital Supply Chain
Cybersecurity
Interoperability and Data Sharing
Improving Research and Development (R&D)
Fighting Counterfeit Drugs
Collaborative Patient Engagement
Online Access to Longitudinal Data by Patient
Off-Chain Data Storage due to Privacy and Data Size
Blockchain Challenges and Solutions for Healthcare Big Data
GDPR versus Blockchain
Problem statement and key factors of GDPR
Solutions
Off-chain blockchain advantages
Off-chain blockchain disadvantages
Conclusion and Discussion
References
Further Reading
9
Intelligence-Based Health Recommendation System Using Big Data Analytics
Introduction
Background
Recommendation System and Its Basic Concepts
Phases of Recommendation System
Methodology
Filtering techniques
Collaborative-based filtering recommendation system
Evaluation of recommendation system
Health Recommendation System
Designing the Health Recommendation System
Framework for HRS
Methods to Design HRS
Evaluation of HRS
Proposed Intelligent-Based HRS
Dataset Description
Experimental Result Analysis
Advantages and Disadvantages of the Proposed Health Recommendation System Using Big Data Analytics
Conclusion and Future Work
References
Further Reading
10
Computational Biology Approach in Management of Big Data of Healthcare Sector
Introduction
Application of Big Data Analysis
Database Management System and Next Generation Sequencing (NGS)
De novo Assembly, Re-Sequencing, Transcriptomics Sequencing and Epigenetics
Data Collection, Extraction of Genes, and Screening of Drugs
Different Algorithms Related to Docking
Molecular Interactions, Scoring Functions, and Discussion of Some Docking Examples
Conclusions
Acknowledgments
References
11
Kidney-Inspired Algorithm and Fuzzy Clustering for Biomedical Data Analysis
Introduction
Biological Structure of the Kidney
Kidney-Inspired Algorithm
Literature Survey
Proposed Model
Fuzzy C-Means Algorithm
Proposed KA-Based Approach for Biomedical Data Analysis
Obtaining optimal cluster centers using KA
Cluster analysis using optimal cluster centers
Results Analysis
Evaluation Metrics
Experimental Results
Statistical Validity
Conclusion
Acknowledgment
References
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
Y
Z
Back Cover

Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data. Examines the methodology and requirements for development of big data architecture, big data modeling, big data as a service, big data analytics, and more Discusses big data applications for intelligent healthcare management, such as revenue management and pricing, predictive analytics/forecasting, big data integration for medical data, algorithms and techniques, etc. Covers the development of big data tools, such as data, web and text mining, data mining, optimization, machine learning, cloud in big data with Hadoop, big data in IoT, and more

2020-05-16