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Submit a ProposalBig Data Analytics and Machine Intelligence in Biomedical and Health Informatics
 | Concepts, Methodologies, Tools, and Applications Edited by Sunil Kumar Dhal, Subhendu Kumar Pani, Srinivas Prasad and Sudhir Kumar Mohapatra Copyright: 2022 | Status: Published ISBN: 9781119791737 | Hardcover | 325 pages | 103 illustrations Price: $225 USD  |
One Line Description
Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics.
Audience
Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.
Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.
Description
The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data.
The 12 chapters in Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things (IoMT).
New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches.
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The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data.
The 12 chapters in Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things (IoMT).
New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches.
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Author / Editor Details
Sunil Kumar Dhal, PhD, is a computer scientist and is Head of Department and professor in the Faculty of Management, Sri Sri University, India. He has more than 20 years of teaching experience with more than 60 international publications including eight books and two patents.
Sunil Kumar Dhal, PhD, is a computer scientist and is Head of Department and professor in the Faculty of Management, Sri Sri University, India. He has more than 20 years of teaching experience with more than 60 international publications including eight books and two patents.
Subhendu Kumar Pani, PhD, is a professor in the Department of Computer Science & Engineering, Orissa Engineering College (OEC) Bhubaneswar, India. He has more than 15 years of teaching and research experience and has published more than 50 international journal articles as well as five authored books, 12 edited books, and eight patents.
Srinivas Prasad, PhD, is a professor in the Department of Computer Science and Engineering at GITAM University, Visakhapatnam, India. He has more than 20 years of teaching experience and published more than 60 publications which include journal articles, conference papers, edited volumes, and book chapters.
Sudhir Kumar Mohapatra, PhD, is an associate professor at Addis Ababa Science & Technology University, Addis Ababa, Ethiopia. Besides 10 years of teaching and research, he spent five years in software development in the banking and education domains.
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Table of Contents
Preface
1. An Introduction to Big Data Analytics Techniques in Healthcare
Anil Audumbar Pise
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)
1.5.7.1 IoT for Health Insurance Companies
1.5.7.2 IoT for Physicians
1.5.7.3 IoT for Hospitals
1.5.7.4 IoT for Patients
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
Sudhir Kumar Mohapatra, Srinivas Prasad, Getachew Mekuria Habtemariam and Mohammed Siddique
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
2.4.6.1 Naïve Bayes Classifier: Prediction on Test Data
2.4.6.2 C5.0 Classifier on Train Dataset
2.4.6.3 Rules From Decision Trees
2.4.6.4 SVM Classifier: Unbalanced and Balanced Train Dataset
2.4.6.5 Random Forest Model: On Train Dataset
2.4.7 Evaluation
2.5 Conclusion
References
3. Pre-Trained CNN Models in Early Alzheimer’s Prediction Using Post-Processed MRI
Kalyani Gunda and Pradeepini Gera
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
Birendra Biswal, Raveendra T., Dwiti Krishna Bebarta, Geetha Pavani P. and P.K. Biswal
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 Hemanta Kumar Bhuyan and Subhendu Kumar Pani
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
Shrikaant Kulkarni
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)
Shanmuk Srinivas Amiripalli, Vishnu Vardhan Reddy Kollu, Ritika Prasad and Mukkamala S.N.V. Jitendra
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
Sowjanya Naidu K. and Srinivasa L. Chakravarthy
8.1 Introduction
8.1.1 Basics of Blockchain Technology
8.1.2 Distributed Consensus Protocol
8.1.3 Smart Contracts
8.1.3.1 How Do Smart Contracts Work?
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 Hemanta Kumar Bhuyan and Subhendu Kumar Pani
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 Amita Verma and Arpit Bansal
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
Manna Sheela Rani Chetty and Kiran Babu C. V.
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
Saraju Prasad and Sunil Dhal
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
Back to Top
Preface
1. An Introduction to Big Data Analytics Techniques in Healthcare
Anil Audumbar Pise
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)
1.5.7.1 IoT for Health Insurance Companies
1.5.7.2 IoT for Physicians
1.5.7.3 IoT for Hospitals
1.5.7.4 IoT for Patients
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
Sudhir Kumar Mohapatra, Srinivas Prasad, Getachew Mekuria Habtemariam and Mohammed Siddique
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
2.4.6.1 Naïve Bayes Classifier: Prediction on Test Data
2.4.6.2 C5.0 Classifier on Train Dataset
2.4.6.3 Rules From Decision Trees
2.4.6.4 SVM Classifier: Unbalanced and Balanced Train Dataset
2.4.6.5 Random Forest Model: On Train Dataset
2.4.7 Evaluation
2.5 Conclusion
References
3. Pre-Trained CNN Models in Early Alzheimer’s Prediction Using Post-Processed MRI
Kalyani Gunda and Pradeepini Gera
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
Birendra Biswal, Raveendra T., Dwiti Krishna Bebarta, Geetha Pavani P. and P.K. Biswal
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 Hemanta Kumar Bhuyan and Subhendu Kumar Pani
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
Shrikaant Kulkarni
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)
Shanmuk Srinivas Amiripalli, Vishnu Vardhan Reddy Kollu, Ritika Prasad and Mukkamala S.N.V. Jitendra
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
Sowjanya Naidu K. and Srinivasa L. Chakravarthy
8.1 Introduction
8.1.1 Basics of Blockchain Technology
8.1.2 Distributed Consensus Protocol
8.1.3 Smart Contracts
8.1.3.1 How Do Smart Contracts Work?
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 Hemanta Kumar Bhuyan and Subhendu Kumar Pani
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 Amita Verma and Arpit Bansal
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
Manna Sheela Rani Chetty and Kiran Babu C. V.
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
Saraju Prasad and Sunil Dhal
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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