Main description:

Big Data Analytics and Medical Information Systems presents the valuable use of artificial intelligence and big data analytics in healthcare and medical sciences. It focuses on theories, methods and approaches in which data analytic techniques can be used to examine medical data to provide a meaningful pattern for classification, diagnosis, treatment, and prediction of diseases. The book discusses topics such as theories and concepts of the field, and how big medical data mining techniques and applications can be applied to classification, diagnosis, treatment, and prediction of diseases. In addition, it covers social, behavioral, and medical fake news analytics to prevent medical misinformation and myths. It is a valuable resource for graduate students, researchers and members of biomedical field who are interested in learning more about analytic tools to support their work.

Contents:

Section I. Theories and Concepts of Big Data Analytics in Healthcare
1. Big data analytics in healthcare: Theory, tools, techniques and its applications
2. Driving impact through big data utilization and analytics in the context of a learning health system
3. Classification of medical big data: A review of systematic analysis of medical big data in real time setup
4. Towards big data framework in government public open data (GPOD) for health

Section II. Big Medical Data: Techniques, Managements, and Applications
5. Big data analytics techniques for healthcare
6. Big data analytics in precision medicine
7. Recent advances in processing, interpreting, and managing biological data for therapeutic intervention of human infectious disease
8. Big data analytics for health: A comprehensive review of techniques and applications

Section III. Diagnosis and Treatment: Big Data Analytical Techniques, Datasets, Life Cycles, Managements and Applications for Diagnosis and Treatment
9. Recent applications of data mining in medical diagnosis and prediction
10. Big medical data analytics for diagnosis
11. Big data analytics and radiomics to discover diagnostics on different cancer types
12. Big medical data, cloud computing and artificial intelligence for improving diagnosis in healthcare

Section IV. Prediction: Big Data Analytical Techniques, Datasets, Life Cycles, Managements and Applications for Prediction
13. Use of artificial intelligence for predicting infectious disease
14. Hospital data analytics system for tracking and predicting obese patients' lifestyle habits
15. Predictions on diabetic patient datasets using big data analytics and machine learning techniques
16. Skin cancer prediction using big data analytics and AI techniques

Section V. Big Medical Fake News Analytics for Preventing Medical Misinformation and Myths
17. COVID-19 fake news analytics from social media using topic modeling and clustering
18. Big medical data mining system (BigMed) for the detection and classification of COVID-19 misinformation

Section VI. Challenges and Future of Big Data in Healthcare
19. Privacy security risks of big data processing in healthcare
20. Opportunities and challenges in healthcare with the management of big biomedical data
21. Future direction for healthcare based on big data analytics

Section VII. Case Studies of Big Data in Healthcare Arena
22. Big data in orthopedics: Between hypes and hopes
23. Predicting onset (type-2) of diabetes from medical records using binary class classification
24. Screening programs incorporating big data analytics