Cognitive Intelligence and Big Data in Healthcare

Cover
Half-Title Page
Title Page
Copyright Page
Contents
Preface
1 Era of Computational Cognitive Techniques in Healthcare Systems
1.1 Introduction
1.2 Cognitive Science
1.3 Gap Between Classical Theory of Cognition
1.4 Cognitive Computing’s Evolution
1.5 The Coming Era of Cognitive Computing
1.6 Cognitive Computing Architecture
1.6.1 The Internet-of-Things and Cognitive Computing
1.6.2 Big Data and Cognitive Computing
1.6.3 Cognitive Computing and Cloud Computing
1.7 Enabling Technologies in Cognitive Computing
1.7.1 Reinforcement Learning and Cognitive Computing
1.7.2 Cognitive Computing with Deep Learning
1.7.2.1 Relational Technique and Perceptual Technique
1.7.2.2 Cognitive Computing and Image Understanding
1.8 Intelligent Systems in Healthcare
1.8.1 Intelligent Cognitive System in Healthcare (Why and How)
1.9 The Cognitive Challenge
1.9.1 Case Study: Patient Evacuation
1.9.2 Case Study: Anesthesiology
1.10 Conclusion
References
2 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics
2.1 Introduction
2.2 Literature Concept
2.2.1 Cognitive Computing Concept
2.2.2 Neural Networks Concepts
2.2.3 Convolutional Neural Network
2.2.4 Deep Learning
2.3 Materials and Methods (Metaheuristic Algorithm Proposal)
2.4 Case Study and Discussion
2.5 Conclusions with Future Research Scopes
References
3 Convergence of Big Data and Cognitive Computing in Healthcare
3.1 Introduction
3.2 Literature Review
3.2.1 Role of Cognitive Computing in Healthcare Applications
3.2.2 Research Problem Study by IBM
3.2.3 Purpose of Big Data in Healthcare
3.2.4 Convergence of Big Data with Cognitive Computing
3.2.4.1 Smart Healthcare
3.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare
3.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification
3.3.1 EEG Pathology Diagnoses
3.3.2 Cognitive–Big Data-Based Smart Healthcare
3.3.3 System Architecture
3.3.4 Detection and Classification of Pathology
3.3.4.1 EEG Preprocessing and Illustration
3.3.4.2 CNN Model
3.3.5 Case Study
3.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud
3.4.1 Cloud Computing with Big Data in Healthcare
3.4.2 Heart Diseases
3.4.3 Healthcare Big Data Techniques
3.4.3.1 Rule Set Classifiers
3.4.3.2 Neuro Fuzzy Classifiers
3.4.3.3 Experimental Results
3.5 Conclusion
References
4 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging
4.1 Introduction
4.2 The Role of Technology in an Aging Society
4.3 Literature Survey
4.4 Health Monitoring
4.5 Nutrition Monitoring
4.6 Stress-Log: An IoT-Based Smart Monitoring System
4.7 Active Aging
4.8 Localization
4.9 Navigation Care
4.10 Fall Monitoring
4.10.1 Fall Detection System Architecture
4.10.2 Wearable Device
4.10.3 Wireless Communication Network
4.10.4 Smart IoT Gateway
4.10.5 Interoperability
4.10.6 Transformation of Data
4.10.7 Analyzer for Big Data
4.11 Conclusion
References
5 Influence of Cognitive Computing in Healthcare Applications
5.1 Introduction
5.2 Bond Between Big Data and Cognitive Computing
5.3 Need for Cognitive Computing in Healthcare
5.4 Conceptual Model Linking Big Data and Cognitive Computing
5.4.1 Significance of Big Data
5.4.2 The Need for Cognitive Computing
5.4.3 The Association Between the Big Data and Cognitive Computing
5.4.4 The Advent of Cognition in Healthcare
5.5 IBM’s Watson and Cognitive Computing
5.5.1 Industrial Revolution with Watson
5.5.2 The IBM’s Cognitive Computing Endeavour in Healthcare
The IBM Watson Health and Watson Health Cloud
Usage of Cognitive Application to Augment the Electronic Medical Record
5.6 Future Directions
5.6.1 Retail
5.6.2 Research
5.6.3 Travel
5.6.4 Security and Threat Detection
5.6.5 Cognitive Training Tools
5.7 Conclusion
References
6 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems
6.1 Introduction
6.2 Literature Concept
6.2.1 Cognitive Computing Concept
6.2.1.1 Application Potential
6.2.2 Cognitive Computing in Healthcare
6.2.3 Deep Learning in Healthcare
6.2.4 Natural Language Processing in Healthcare
6.3 Discussion
6.4 Trends
6.5 Conclusions
References
7 Protecting Patient Data with 2FAuthentication
7.1 Introduction
7.2 Literature Survey
7.3 Two-Factor Authentication
7.3.1 Novel Features of Two-Factor Authentication
7.3.2 Two-Factor Authentication Sorgen
7.3.3 Two-Factor Security Libraries
7.3.4 Challenges for Fitness Concern
7.4 Proposed Methodology
7.5 Medical Treatment and the Preservation of Records
7.5.1 Remote Method of Control
7.5.2 Enabling Healthcare System Technology
7.6 Conclusion
References
8 Data Analytics for Healthcare Monitoring and Inferencing
8.1 An Overview of Healthcare Systems
8.2 Need of Healthcare Systems
8.3 Basic Principle of Healthcare Systems
8.4 Design and Recommended Structure of Healthcare Systems
8.4.1 Healthcare System Designs on the Basis of these Parameters
8.4.2 Details of Healthcare Organizational Structure
8.5 Various Challenges in Conventional Existing Healthcare System
8.6 Health Informatics
8.7 Information Technology Use in Healthcare Systems
8.8 Details of Various Information Technology Application Use in Healthcare Systems
8.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below
8.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems
8.11 Healthcare Data Analytics
8.12 Healthcare as a Concept
8.13 Healthcare’s Key Technologies
8.14 The Present State of Smart Healthcare Application
8.15 Data Analytics with Machine Learning Use in Healthcare Systems
8.16 Benefit of Data Analytics in Healthcare System
8.17 Data Analysis and Visualization: COVID-19 Case Study in India
8.18 Bioinformatics Data Analytics
8.18.1 Notion of Bioinformatics
8.18.2 Bioinformatics Data Challenges
8.18.3 Sequence Analysis
8.18.4 Applications
8.18.5 COVID-19: A Bioinformatics Approach
8.19 Conclusion
References
9 Features Optimistic Approach for the Detection of Parkinson’s Disease
9.1 Introduction
9.1.1 Parkinson’s Disease
9.1.2 Spect Scan
9.2 Literature Survey
9.3 Methods and Materials
9.3.1 Database Details
9.3.2 Procedure
9.3.3 Pre-Processing Done by PPMI
9.3.4 Image Analysis and Features Extraction
9.3.4.1 Image Slicing
9.3.4.2 Intensity Normalization
9.3.4.3 Image Segmentation
9.3.4.4 Shape Features Extraction
9.3.4.5 SBR Features
9.3.4.6 Feature Set Analysis
9.3.4.7 Surface Fitting
9.3.5 Classification Modeling
9.3.6 Feature Importance Estimation
9.3.6.1 Need for Analysis of Important Features
9.3.6.2 Random Forest
9.4 Results and Discussion
9.4.1 Segmentation
9.4.2 Shape Analysis
9.4.3 Classification
9.5 Conclusion
References
10 Big Data Analytics in Healthcare
10.1 Introduction
10.2 Need for Big Data Analytics
10.3 Characteristics of Big Data
10.3.1 Volume
10.3.2 Velocity
10.3.3 Variety
10.3.4 Veracity
10.3.5 Value
10.3.6 Validity
10.3.7 Variability
10.3.8 Viscosity
10.3.9 Virality
10.3.10 Visualization
10.4 Big Data Analysis in Disease Treatment and Management
10.4.1 For Diabetes
10.4.2 For Heart Disease
10.4.3 For Chronic Disease
10.4.4 For Neurological Disease
10.4.5 For Personalized Medicine
10.5 Big Data: Databases and Platforms in Healthcare
10.6 Importance of Big Data in Healthcare
10.6.1 Evidence-Based Care
10.6.2 Reduced Cost of Healthcare
10.6.3 Increases the Participation of Patients in the Care Process
10.6.4 The Implication in Health Surveillance
10.6.5 Reduces Mortality Rate
10.6.6 Increase of Communication Between Patients and Healthcare Providers
10.6.7 Early Detection of Fraud and Security Threats in Health Management
10.6.8 Improvement in the Care Quality
10.7 Application of Big Data Analytics
10.7.1 Image Processing
10.7.2 Signal Processing
10.7.3 Genomics
10.7.4 Bioinformatics Applications
10.7.5 Clinical Informatics Application
10.8 Conclusion
References
11 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery
11.1 Introduction
11.1.1 Glaucoma
11.2 Literature Survey
11.3 Methodology
11.3.1 Sclera Segmentation
11.3.1.1 Fully Convolutional Network
11.3.2 Pupil/Iris Ratio
11.3.2.1 Canny Edge Detection
11.3.2.2 Mean Redness Level (MRL)
MBP Mean Blue Mean S m S
11.3.2.3 Red Area Percentage (RAP)
11.4 Results and Discussion
11.4.1 Feature Extraction from Frontal Eye Images
11.4.1.1 Level of Mean Redness (MRL)
11.4.1.2 Percentage of Red Area (RAP)
11.4.2 Images of the Frontal Eye Pupil/Iris Ratio
11.4.2.1 Histogram Equalization
11.4.2.2 Morphological Reconstruction
11.4.2.3 Canny Edge Detection
11.4.2.4 Adaptive Thresholding
11.4.2.5 Circular Hough Transform
11.4.2.6 Classification
11.5 Conclusion and Future Work
References
12 State of Mental Health and Social Media: Analysis, Challenges, Advancements
12.1 Introduction
12.2 Introduction to Big Data and Data Mining
12.3 Role of Sentimental Analysis in the Healthcare Sector
12.4 Case Study: Analyzing Mental Health
12.4.1 Problem Statement
12.4.2 Research Objectives
12.4.3 Methodology and Framework
12.4.3.1 Big 5 Personality Model
12.4.3.2 Openness to Explore
12.4.3.3 Methodology
12.4.3.4 Detailed Design Methodologies
12.4.3.5 Work Done Details as Required
12.5 Results and Discussion
12.6 Conclusion and Future
References
13 Applications of Artificial Intelligence, Blockchain, and Internet-of-Things in Management of Chronic Disease
13.1 Introduction
13.2 Artificial Intelligence and Management of Chronic Diseases
13.3 Blockchain and Healthcare
13.3.1 Blockchain and Healthcare Management of Chronic Disease
13.4 Internet-of-Things and Healthcare Management of Chronic Disease
13.5 Conclusions
References
14 Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain
14.1 Introduction
14.2 Cognitive Computing Framework in Healthcare
14.3 Benefits of Using Cognitive Computing for Healthcare
14.4 Applications of Deploying Cognitive Assisted Technology in Healthcare Management
14.4.1 Using Cognitive Services for a Patient’s Healthcare Management
14.4.2 Using Cognitive Services for Healthcare Providers
14.5 Challenges in Using the Cognitive Assistive Technology in Healthcare Management
14.6 Future Directions for Extending Heathcare Services Using CATs
14.7 Addressing CAT Challenges in Healthcare as a General Framework
14.8 Conclusion
References
Index
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