Big Data Analytics for Healthcare
Datasets, Techniques, Life Cycles, Management, and Applications
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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.
Key Features
- Presents theories, methods and approaches in which data analytic techniques are used for medical data
- Brings practical information on how to use big data for classification, diagnosis, treatment, and prediction of diseases
- Discusses social, behavioral, and medical fake news analytics for medical information systems
Readership
Graduate students and researchers on medical informatics; Data scientists, computer scientists
Table of 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
Product details
- No. of pages: 354
- Language: English
- Copyright: © Academic Press 2022
- Published: May 19, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780323919074
- eBook ISBN: 9780323985161
About the Editor
Pantea Keikhosrokiani
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 with Bachelor of Science in Electrical Engineering Electronics. Her recent book is published in 2019 entitled “Perspectives in the Development of Mobile Medical Information Systems: Life Cycle, Management, Methodological Approach and Application”. Her articles have been published in distinguished edited books and journals by Elsevier, Springer, Taylors and Francis, IGI global, etc. and have been indexed by ISI, Scopus and PubMed. Her areas of interest for research and teaching are Information Systems & Analytics, Health, Medical & Business Analytics, Big Data, Behavioral analytics, Opinion mining, Digital Informatics, and Digital Technopreneurship.
Affiliations and Expertise
Senior Lecturer, School of Computer Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia
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