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Book Cover
E-book
Author Keikhosrokiani, Pantea

Title Big Data Analytics for Healthcare Datasets, Techniques, Life Cycles, Management, and Applications
Published San Diego : Elsevier Science & Technology, 2022

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Description 1 online resource (356 p.)
Contents Front Cover -- Big Data Analytics for Healthcare -- Big Data Analytics for Healthcare: Datasets, Techniques, Life Cycles, Management, and Applications -- Copyright -- Contents -- Contributors -- Preface -- 1. Overview of big medical data and analytical infrastructure for decision-making in healthcare: healthcare intelligence -- 2. Purpose of the book -- 3. Organization of the book -- 3.1 Section I: Theories and concepts of big data analytics in healthcare -- 3.2 Section II: Big medical data: Techniques, managements, and applications
3.3 Section III: Diagnosis and treatment: Big data analytical techniques, datasets, life cycles, managements, and applications ... -- 3.4 Section IV: Prediction: Big data analytical techniques, datasets, life cycles, managements, and applications for prediction -- 3.5 Section V: Big medical fake news analytics for preventing medical misinformation and myths -- 3.6 Section VI: Challenges and future of big data in healthcare -- 3.7 Section VII: Case studies of big data in healthcare arena -- REFERENCES -- I -- Theories and concepts of big data analytics in healthcare
1 -- Big data analytics in healthcare: theory, tools, techniques and its applications -- 1. Introduction -- 1.1 Volume -- 1.1.1 Impact of increasing volume in healthcare data -- 1.2 Velocity -- 1.2.1 Impact of increasing velocity in healthcare data -- 1.3 Variety -- 1.3.1 Impact of increasing variety in healthcare data -- 1.4 Other factors influencing in big data -- 2. Challenges in big data analytics -- 2.1 Management of big data -- 2.2 Security and privacy concerns -- 2.3 Scalability -- 3. Data analytics life cycle -- 3.1 Data generation -- 3.1.1 Structured data -- 3.1.2 Unstructured data
3.1.3 Semistructured data -- 3.1.4 Quasi structured data -- 3.2 Big data acquisition in healthcare -- 4. Data analytics during the Covid-19 pandemic -- 5. Big data tools in healthcare -- 5.1 Big data management and analysis -- 6. Summary -- References -- Further reading -- 2 -- Driving impact through big data utilization and analytics in the context of a Learning Health System -- 1. Introduction -- 2. What matters for healthcare? -- 3. Global strategies for impact on health -- 4. What is big data? -- 5. Applying big data-precision medicine -- 6. Learning Health System-a paradigm for the future?
7. Driving big data utilization in an LHS -- 8. Challenges -- 9. Conclusion -- Funding statement -- References -- 3 -- Classification of medical big data: a review of systematic analysis of medical big data in real-time setup -- 1. Introduction -- 2. Types of data -- 2.1 Attributes of big data -- 2.2 Core big data analysis strategies -- 2.2.1 A or B testing -- 2.2.2 Unification of data -- 2.2.3 Data feature extraction or mining of data -- 2.2.4 Artificial intelligence and machine learning -- 2.2.5 Math and statistics -- 3. Accountancy of big data analytics in health care domains
Notes Description based upon print version of record
3.1 Hospital and health hubs
Form Electronic book
ISBN 9780323985161
0323985165