Big Data Analytics for Healthcare

Datasets, Techniques, Life Cycles, Management, and Applications
Herausgeber: Keikhosrokiani, Pantea

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Elsevier Science & Technology
• Seitenzahl: 354
• Erscheinungstermin: 24. Mai 2022
• Englisch
• Abmessung: 275mm x 301mm x 25mm
• Gewicht: 954g
• ISBN-13: 9780323919074
• ISBN-10: 0323919073
• Artikelnr.: 63246007

Inhaltsangabe

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 Healthcare
19. 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