Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics

Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics

by Sunil Kumar Dhal, Srinivas Prasad, Sudhir Kumar Mohapatra, Subhendu Kumar Pani
Released June 2022
Publisher(s): Wiley-Scrivener
ISBN: 9781119791737

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Book description

BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS

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.

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.

Audience

Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Preface
  5. 1 An Introduction to Big Data Analytics Techniques in Healthcare
    1. 1.1 Introduction
    2. 1.2 Big Data in Healthcare
    3. 1.3 Areas of Big Data Analytics in Medicine
    4. 1.4 Healthcare as a Big Data Repository
    5. 1.5 Applications of Healthcare Big Data
    6. 1.6 Challenges in Big Data Analytics
    7. 1.7 Big Data Privacy and Security
    8. 1.8 Conclusion
    9. 1.9 Future Work
    10. References
  6. 2 Identify Determinants of Infant and Child Mortality Based Using Machine Learning: Case Study on Ethiopia
    1. 2.1 Introduction
    2. 2.2 Literature Review
    3. 2.3 Methodology and Data Source
    4. 2.4 Implementation and Results
    5. 2.5 Conclusion
    6. References
  7. 3 Pre-Trained CNN Models in Early Alzheimer’s Prediction Using Post-Processed MRI
    1. 3.1 Introduction
    2. 3.2 Experimental Study
    3. 3.3 Data Exploration
    4. 3.4 OASIS Dataset Pre-Processing
    5. 3.5 Alzheimer’s 4-Class-MRI Features Extraction
    6. 3.6 Alzheimer 4-Class MRI Image Dataset
    7. 3.7 RMSProp (Root Mean Square Propagation)
    8. 3.8 Activation Function
    9. 3.9 Batch Normalization
    10. 3.10 Dropout
    11. 3.11 Result—I
    12. 3.12 Conclusion and Future Work
    13. Acknowledgement
    14. References
  8. 4 Robust Segmentation Algorithms for Retinal Blood Vessels, Optic Disc, and Optic Cup of Retinal Images in Medical Imaging
    1. 4.1 Introduction
    2. 4.2 Basics of Proposed Methods
    3. 4.3 Experimental Results and Discussion
    4. 4.4 Conclusion
    5. References
  9. 5 Analysis of Healthcare Systems Using Computational Approaches
    1. 5.1 Introduction
    2. 5.2 AI & ML Analysis in Health Systems
    3. 5.3 Healthcare Intellectual Approaches
    4. 5.4 Precision Approaches to Medicine
    5. 5.5 Methodology of AI, ML With Healthcare Examples
    6. 5.6 Big Analytic Data Tools
    7. 5.7 Discussion
    8. 5.8 Conclusion
    9. References
  10. 6 Expert Systems in Behavioral and Mental Healthcare: Applications of AI in Decision-Making and Consultancy
    1. 6.1 Introduction
    2. 6.2 AI Methods
    3. 6.3 Turing Test
    4. 6.4 Barriers to Technologies
    5. 6.5 Advantages of AI for Behavioral & Mental Healthcare
    6. 6.6 Enhanced Self-Care & Access to Care
    7. 6.7 Other Considerations
    8. 6.8 Expert Systems in Mental & Behavioral Healthcare
    9. 6.9 Dynamical Approaches to Clinical AI and Expert Systems
    10. 6.10 Conclusion
    11. 6.11 Future Prospects
    12. References
  11. 7 A Mathematical-Based Epidemic Model to Prevent and Control Outbreak of Corona Virus 2019 (COVID-19)
    1. 7.1 Introduction
    2. 7.2 Related Work
    3. 7.3 Proposed Frameworks
    4. 7.4 Results and Discussion
    5. 7.5 Conclusion
    6. References
  12. 8 An Access Authorization Mechanism for Electronic Health Records of Blockchain to Sheathe Fragile Information
    1. 8.1 Introduction
    2. 8.2 Related Work
    3. 8.3 Need for Blockchain in Healthcare
    4. 8.4 Proposed Frameworks
    5. 8.5 Use Cases
    6. 8.6 Discussions
    7. 8.7 Challenges and Limitations
    8. 8.8 Future Work
    9. 8.9 Conclusion
    10. References
  13. 9 An Epidemic Graph’s Modeling Application to the COVID-19 Outbreak
    1. 9.1 Introduction
    2. 9.2 Related Work
    3. 9.3 Theoretical Approaches
    4. 9.4 Frameworks
    5. 9.5 Evaluation of COVID-19 Outbreak
    6. 9.6 Conclusions and Future Works
    7. References
  14. 10 Big Data and Data Mining in e-Health: Legal Issues and Challenges
    1. 10.1 Introduction
    2. 10.2 Big Data and Data Mining in e-Health
    3. 10.3 Big Data and e-Health in India
    4. 10.4 Legal Issues Arising Out of Big Data and Data Mining in e-Health
    5. 10.5 Big Data and Issues of Privacy in e-Health
    6. 10.6 Conclusion and Suggestions
    7. References
  15. 11 Basic Scientific and Clinical Applications
    1. 11.1 Introduction
    2. 11.2 Case Study-1: Continual Learning Using ML for Clinical Applications
    3. 11.3 Case Study-2
    4. 11.4 Case Study-3: ML Will Improve the RadiologyPatient Experience
    5. 11.5 Case Study-4: Medical Imaging AI with Transition from Academic Research to Commercialization
    6. 11.6 Case Study-5: ML will Benefit All Medical Imaging ‘ologies’
    7. 11.7 Case Study-6: Health Providers will Leverage Data Hubs to Unlock the Value of Their Data
    8. 11.8 Conclusion
    9. References
  16. 12 Healthcare Branding Through Service Quality
    1. 12.1 Introduction to Healthcare
    2. 12.2 Quality in Healthcare
    3. 12.3 Service Quality
    4. 12.4 Conclusion and Road Ahead
    5. References
  17. Index
  18. End User License Agreement

Product information

  • Title: Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics
  • Author(s): Sunil Kumar Dhal, Srinivas Prasad, Sudhir Kumar Mohapatra, Subhendu Kumar Pani
  • Release date: June 2022
  • Publisher(s): Wiley-Scrivener
  • ISBN: 9781119791737