Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics: Concepts, Methodologies, Tools and Applications

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 An Introduction to Big Data Analytics Techniques in Healthcare
	1.1 Introduction
	1.2 Big Data in Healthcare
	1.3 Areas of Big Data Analytics in Medicine
		1.3.1 Genomics
		1.3.2 Signal Processing
		1.3.3 Image Processing
	1.4 Healthcare as a Big Data Repository
	1.5 Applications of Healthcare Big Data
		1.5.1 Electronic Health Records (EHRs)
		1.5.2 Telemedicine
		1.5.3 NoSQL Database
		1.5.4 Framework for Reconstructing Epidemiological Dynamics (FRED)
		1.5.5 Advanced Risk and Disease Management
		1.5.6 Digital Epidemiology
		1.5.7 Internet of Things (IoT)
		1.5.8 Improved Supply Chain Management
		1.5.9 Developing New Therapies and Innovations
	1.6 Challenges in Big Data Analytics
	1.7 Big Data Privacy and Security
	1.8 Conclusion
	1.9 Future Work
	References
2 Identify Determinants of Infant and Child Mortality Based Using Machine Learning: Case Study on Ethiopia
	2.1 Introduction
	2.2 Literature Review
	2.3 Methodology and Data Source
		2.3.1 Study Area
		2.3.2 Source of Data
		2.3.3 Variables Included in the Study
		2.3.4 Building a Predictive Model
	2.4 Implementation and Results
		2.4.1 Missing Value Handling
		2.4.2 Feature Selection Methods
		2.4.3 Features Importance Rank
		2.4.4 Data Split
		2.4.5 Imbalanced Data Handling
		2.4.6 Make Predictions on Unseen Test Data
		2.4.7 Evaluation
	2.5 Conclusion
	References
3 Pre-Trained CNN Models in Early Alzheimer’s Prediction Using Post-Processed MRI
	3.1 Introduction
		3.1.1 Background
	3.2 Experimental Study
		3.2.1 OASIS Longitudinal Data
			3.2.1.1 Feature Characteristics
		3.2.2 Alzheimer’s 4-Class-MRI-Dataset
	3.3 Data Exploration
		3.3.1 Features Description
	3.4 OASIS Dataset Pre-Processing
		3.4.1 Features Selection
		3.4.2 Feature Transform
			3.4.2.1 MinMaxScaler
		3.4.3 Model Selection
			3.4.3.1 Decision Tree Classification
			3.4.3.2 Ensemble Machine Learning
			3.4.3.3 Random Forest Classifier
		3.4.4 Model Fitting
		3.4.5 Evaluation Metric/Model Evaluation
	3.5 Alzheimer’s 4-Class-MRI Features Extraction
	3.6 Alzheimer 4-Class MRI Image Dataset
		3.6.1 Image Processing
		3.6.2 Classification of 4-CLASS-MRI
			3.6.2.1 AlexNet
			3.6.2.2 VGG-16
			3.6.2.3 Inception (GoogLeNet)
			3.6.2.4 Residual Network (“RESNET”)
			3.6.2.5 MobileNetV2
			3.6.2.6 NASANet (Neural Architecture Search Network)
	3.7 RMSProp (Root Mean Square Propagation)
	3.8 Activation Function
	3.9 Batch Normalization
	3.10 Dropout
	3.11 Result—I
		3.11.1 Result—II
	3.12 Conclusion and Future Work
	Acknowledgement
	References
4 Robust Segmentation Algorithms for Retinal Blood Vessels, Optic Disc, and Optic Cup of Retinal Images in Medical Imaging
	4.1 Introduction
	4.2 Basics of Proposed Methods
	4.3 Experimental Results and Discussion
	4.4 Conclusion
	References
5 Analysis of Healthcare Systems Using Computational Approaches
	5.1 Introduction
		5.1.1 Diagnosis Process in Healthcare Systems
		5.1.2 Issues of Healthcare
		5.1.3 Clinical Diagnosis Based on Soft Computing
			5.1.3.1 Neural Network and Fuzzy Healthcare Systems
			5.1.3.2 Systems of Fuzzy-Genetic Algorithms (F-GA)
			5.1.3.3 Genetic Algorithm Systems and Neural Networks (NNGA)
			5.1.3.4 Genetic Algorithm, Fuzzy Logic and Neural Network (NN-FL-GA)
			5.1.3.5 Tool for Big Data Analytics
	5.2 AI & ML Analysis in Health Systems
	5.3 Healthcare Intellectual Approaches
		5.3.1 AI and ML Roles in the Healthcare System
		5.3.2 Medical ML Medicine
		5.3.3 Clinical System Growth
		5.3.4 Clinical Data Development Using AI
		5.3.5 EHR Disease Detection
		5.3.6 Cognitive Cancer Approaches
		5.3.7 Effective EHR Operations
		5.3.8 Deep Learning Approach (DL) in the Clinical System
		5.3.9 Healthcare Data Transformation
		5.3.10 Prediction of Cancer
	5.4 Precision Approaches to Medicine
		5.4.1 EMR Analysis Medicine
		5.4.2 AI-Based Medicine Accuracy
		5.4.3 Tumor Cell Visual Evaluation
	5.5 Methodology of AI, ML With Healthcare Examples
	5.6 Big Analytic Data Tools
		5.6.1 Hadoop-Based Health Industry Tools
		5.6.2 Healthcare System Architecture
	5.7 Discussion
	5.8 Conclusion
	References
6 Expert Systems in Behavioral and Mental Healthcare: Applications of AI in Decision-Making and Consultancy
	6.1 Introduction
	6.2 AI Methods
		6.2.1 Machine Learning & Artificial Neural Networks (ML & ANN)
		6.2.2 Natural Language Processing (NLP)
		6.2.3 Machine Perception & Sensing
		6.2.4 Affective Computing
		6.2.5 Virtual & Augmented Reality (VR & AR)
		6.2.6 Cloud Computing & Wireless Technology
		6.2.7 Robotics
		6.2.8 Brain–Computer Interfaces (BCIs)
		6.2.9 Supercomputing & Simulation of Brain
	6.3 Turing Test
	6.4 Barriers to Technologies
	6.5 Advantages of AI for Behavioral & Mental Healthcare
	6.6 Enhanced Self-Care & Access to Care
		6.6.1 Care Customization
		6.6.2 Economic Benefits
	6.7 Other Considerations
	6.8 Expert Systems in Mental & Behavioral Healthcare
		6.8.1 Historical Perspectives
	6.9 Dynamical Approaches to Clinical AI and Expert Systems
		6.9.1 Temporal Modeling
		6.9.2 Practical Global Clinical Applications
		6.9.3 Multi-Agent Model Dedicated to Personalized Medicine
		6.9.4 Technology-Enabled Clinicians
		6.9.5 Overview of Dynamical Approaches
		6.9.6 Cognitive Computing in Healthcare
		6.9.7 Emerging Technologies & Clinical AI
		6.9.8 Ethics and Futuristic Challenges
	6.10 Conclusion
	6.11 Future Prospects
	References
7 A Mathematical-Based Epidemic Model to Prevent and Control Outbreak of Corona Virus 2019 (COVID-19)
	7.1 Introduction
		7.1.1 Corona Viruses
		7.1.2 Epidemiological Modeling Using Graph Theory
	7.2 Related Work
	7.3 Proposed Frameworks
		7.3.1 Infection Spreading Model
		7.3.2 Relation between Recovery Time and Interaction of Antivirus Nodes
		7.3.3 Proposed Algorithm
		7.3.4 Detail Explanation of Algorithm
	7.4 Results and Discussion
	7.5 Conclusion
	References
8 An Access Authorization Mechanism for Electronic Health Records of Blockchain to Sheathe Fragile Information
	8.1 Introduction
		8.1.1 Basics of Blockchain Technology
		8.1.2 Distributed Consensus Protocol
		8.1.3 Smart Contracts
		8.1.4 Ethereum and Smart Contracts
	8.2 Related Work
	8.3 Need for Blockchain in Healthcare
	8.4 Proposed Frameworks
	8.5 Use Cases
	8.6 Discussions
	8.7 Challenges and Limitations
	8.8 Future Work
	8.9 Conclusion
	References
9 An Epidemic Graph’s Modeling Application to the COVID-19 Outbreak
	9.1 Introduction
	9.2 Related Work
	9.3 Theoretical Approaches
		9.3.1 Graph Convolutional Networks
		9.3.2 Recurrent Neural Networks
		9.3.3 Epidemic Modeling
	9.4 Frameworks
		9.4.1 Use the Data Model
		9.4.2 Problem Formulation
		9.4.3 Proposed Architecture
	9.5 Evaluation of COVID-19 Outbreak
		9.5.1 Used Datasets
		9.5.2 Evolving an Epidemic
		9.5.3 Predicted Analysis of the Infected Individuals
	9.6 Conclusions and Future Works
	References
10 Big Data and Data Mining in e-Health: Legal Issues and Challenges
	Object of Study
	10.1 Introduction
	10.2 Big Data and Data Mining in e-Health
	10.3 Big Data and e-Health in India
	10.4 Legal Issues Arising Out of Big Data and Data Mining in e-Health
		10.4.1 Right to Privacy
		10.4.2 Data Privacy Laws
		10.4.3 Liability of the Intermediary
	10.5 Big Data and Issues of Privacy in e-Health
	10.6 Conclusion and Suggestions
	References
11 Basic Scientific and Clinical Applications
	11.1 Introduction
	11.2 Case Study-1: Continual Learning Using ML for Clinical Applications
	11.3 Case Study-2
	11.4 Case Study-3: ML Will Improve the Radiology Patient Experience
	11.5 Case Study-4: Medical Imaging AI with Transition from Academic Research to Commercialization
	11.6 Case Study-5: ML will Benefit All Medical Imaging ‘ologies’
	11.7 Case Study-6: Health Providers will Leverage Data Hubs to Unlock the Value of Their Data
	11.8 Conclusion
	References
12 Healthcare Branding Through Service Quality
	12.1 Introduction to Healthcare
	12.2 Quality in Healthcare
		12.2.1 Developing Countries Healthcare Service Quality
		12.2.2 Affordability of Quality in Healthcare
		12.2.3 Dimensions of Healthcare Service
		12.2.4 Healthcare Brand Image
		12.2.5 Patients’ Satisfaction
		12.2.6 Patients’ Loyalty
	12.3 Service Quality
		12.3.1 Patient Loyalty with Service Quality in Healthcare
		12.3.2 Healthcare Policy
	12.4 Conclusion and Road Ahead
	References
Index
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