Big Data Analytics for Healthcare

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.

• Section I. Theories and Concepts of Big Data Analytics in Healthcare1. Big data analytics in healthcare: Theory, tools, techniques and its applications2. Driving impact through big data utilization and analytics in the context of a learning health system3. Classification of medical big data: A review of systematic analysis of medical big data in real time setup4. Towards big data framework in government public open data (GPOD) for healthSection II. Big Medical Data: Techniques, Managements, and Applications5. Big data analytics techniques for healthcare6. Big data analytics in precision medicine7. Recent advances in processing, interpreting, and managing biological data for therapeutic intervention of human infectious disease8. Big data analytics for health: A comprehensive review of techniques and applicationsSection III. Diagnosis and Treatment: Big Data Analytical Techniques, Datasets, Life Cycles, Managements and Applications for Diagnosis and Treatment9. Recent applications of data mining in medical diagnosis and prediction10. Big medical data analytics for diagnosis11. Big data analytics and radiomics to discover diagnostics on different cancer types12. Big medical data, cloud computing and artificial intelligence for improving diagnosis in healthcareSection IV. Prediction: Big Data Analytical Techniques, Datasets, Life Cycles, Managements and Applications for Prediction13. Use of artificial intelligence for predicting infectious disease14. Hospital data analytics system for tracking and predicting obese patients' lifestyle habits15. Predictions on diabetic patient datasets using big data analytics and machine learning techniques16. Skin cancer prediction using big data analytics and AI techniquesSection V. Big Medical Fake News Analytics for Preventing Medical Misinformation and Myths17. COVID-19 fake news analytics from social media using topic modeling and clustering18. Big medical data mining system (BigMed) for the detection and classification of COVID-19 misinformation Section VI. Challenges and Future of Big Data in Healthcare19. Privacy security risks of big data processing in healthcare20. Opportunities and challenges in healthcare with the management of big biomedical data21. Future direction for healthcare based on big data analyticsSection VII. Case Studies of Big Data in Healthcare Arena22. Big data in orthopedics: Between hypes and hopes23. Predicting onset (type-2) of diabetes from medical records using binary class classification24. Screening programs incorporating big data analytics