Human-AI-Cognitive Intelligence Software, System, and Services for Quality Assurance. Theories and Methods with Systems Thinking for Behaviour Change in the Medical Decision-Making Pathway

Habit-based Behavior Change Medical Information Support System and Artificial Intelligence: Theories, Methods, and Data Analytics Approach provides a guideline to design and implement Habit-based Behavior Change Support Systems (HBCSS) which can change patient’s unhealthy habits to prevent the development of diseases. It presents theories, methods, management, and data analytics approach required to design, implement, and prescribe the use of HBCSS for several diseases’ management.It discusses topics such as theories of behavior change, ontologies and knowledge management, data mining, privacy and security, descriptive and prescription analytics. In addition, it discusses how to measure habit-change, future directions of the field, and case studies based on real-world examples.It is a valuable resource for clinicians, researchers, students, and member of the biomedical and medical fields who want to learn more about the use of medical systems to improve patients’ health.

Section 1: Introduction to Habit-based Behavior Change Support Systems (HBCSS)
1. Theories for Habit-based Behavior Change Support Systems (HBCSS)
2. Methodologies for Habit-based Behavior Change Support Systems (HBCSS)
3. Management of Habit-based Behavior Change Support Systems (HBCSS)
4. Features of Habit-based Behavior Change Support Systems (HBCSS)

Section 2: Habit-change Life Cycles for Developing HBCSS
5. Requirements for HBCSS
6. Bad Habit Identification
7. Breaking Bad Habits
8. New Habit Formation
9. Measuring Habit-Change
10. Evaluating Habit-Change
11. Development of HBCSS
12. Securing HBCSS

Section 3: Measuring Habit-Change in Habit-based Behavior Change Support Systems (HBCSS) using Data Analytics
13. Descriptive Analytics for Habit-Change
14. Classification for Habit-Change
15. Clustering for Habit-Change
16. Prediction for Habit-Change
17. Prescription Analytics for HBCSS
18. Databases for HBCSS
19. Conclusion and Future Direction
20. Case Studies

Authors

Pantea Keikhosrokiani Senior Lecturer, School of Computer Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia. Pantea Keikhosrokiani is a Senior Lecturer at the School of Computer Sciences, Universiti Sains Malaysia (USM; Penang, Malaysia). She was a teaching fellow at the National Advanced IPv6 Centre of Excellence (Nav6), USM. She has received her PhD in Service System Engineering, Information System, and her master's degree in information technology from the School of Computer Sciences, USM. She has been graduated in Bachelor of Science in Electrical Engineering Electronics. Her articles have been published in distinguished edited books and journals including Elsevier (Telematics & Informatics), Springer (Cognition, Technology, & Work), Taylors and Francis and IGI global, and have been indexed by ISI, Scopus and PubMed. Her recent book is published by Elsevier entitled Perspectives in The Development of Mobile Medical Information Systems: Life Cycle, Management, Methodological Approach and Application. Her areas of interest for research and teaching are Information Systems Development, Behavior-change support systems, Database Systems, Health and Medical Informatics, Business Informatics, Location-Based Mobile Applications, Big Data, and Technopreneurship.

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