Big Data Analytics for Healthcare: Datasets, Techniques, Life Cycles, Management, and Applications

  • 9h 7m
  • Pantea Keikhosrokiani
  • Elsevier Science and Technology Books, Inc.
  • 2022

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.

  • 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

About the Author

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.

In this Book

  • Big Data Analytics in Healthcare: Theory, Tools, Techniques and Its Applications
  • Driving Impact Through Big Data Utilization and Analytics in the Context of a Learning Health System
  • Classification of Medical Big Data: A Review of Systematic Analysis of Medical Big Data in Real-Time Setup
  • Towards Big Data Framework in Government Public Open Data (GPOD) for Health
  • Big Data Analytics Techniques for Healthcare
  • Big Data Analytics in Precision Medicine
  • Recent Advances in Processing, Interpreting, and Managing Biological Data for Therapeutic Intervention of Human Infectious Disease
  • Big Data Analytics for Health: A Comprehensive Review of Techniques and Applications
  • Recent Applications of Data Mining in Medical Diagnosis and Prediction
  • Big Medical Data Analytics for Diagnosis
  • Big Data Analytics and Radiomics to Discover Diagnostics on Different Cancer Types
  • Big Medical Data, Cloud Computing, and Artificial Intelligence for Improving Diagnosis in Healthcare
  • Use of Artificial Intelligence for Predicting Infectious Disease
  • Hospital Data Analytics System for Tracking and Predicting Obese Patients' Lifestyle Habits
  • Predictions on Diabetic Patient Datasets Using Big Data Analytics and Machine Learning Techniques
  • Skin Cancer Prediction Using Big Data Analytics and AI Techniques
  • COVID-19 Fake News Analytics from Social Media Using Topic Modeling and Clustering
  • Big Medical Data Mining System (BigMed) for the Detection and Classification of COVID-19 Misinformation
  • Privacy Security Risks of Big Data Processing in Healthcare
  • Opportunities and Challenges in Healthcare with the Management of Big Biomedical Data
  • Future Direction for Healthcare Based on Big Data Analytics
  • Big Data in Orthopedics: Between Hypes and Hopes
  • Predicting Onset (Type-2) of Diabetes from Medical Records Using Binary Class Classification
  • Screening Programs Incorporating Big Data Analytics


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