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.
1. Big Data Analytics for Healthcare - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xiii 7. Preface - p. xvii 8. Chapter 1 Big data analytics in healthcare: theory, tools, techniques and its applications - p. 3 9. Chapter 2 Driving impact through big data utilization and analytics in the context of a Learning Health System - p. 13 10. Chapter 3 Classification of medical big data: a review of systematic analysis of medical big data in real-time setup - p. 23 11. Chapter 4 Towards big data framework in government public open data (GPOD) for health - p. 33 12. Chapter 5 Big data analytics techniques for healthcare - p. 49 13. Chapter 6 Big data analytics in precision medicine - p. 63 14. Chapter 7 Recent advances in processing, interpreting, and managing biological data for therapeutic intervention of human infectious disease - p. 73 15. Chapter 8 Big data analytics for health: a comprehensive review of techniques and applications - p. 83 16. Chapter 9 Recent applications of data mining in medical diagnosis and prediction - p. 95 17. Chapter 10 Big medical data analytics for diagnosis - p. 111 18. Chapter 11 Big data analytics and radiomics to discover diagnostics on different cancer types - p. 125 19. Chapter 12 Big medical data, cloud computing, and artificial intelligence for improving diagnosis in healthcare - p. 139 20. Chapter 13 Use of artificial intelligence for predicting infectious disease - p. 153 21. Chapter 14 Hospital data analytics system for tracking and predicting obese patients' lifestyle habits - p. 165 22. Chapter 15 Predictions on diabetic patient datasets using big data analytics and machine learning techniques - p. 179 23. Chapter 16 Skin cancer prediction using big data analytics and AI techniques - p. 201 24. Chapter 17 COVID-19 fake news analytics from social media using topic modeling and clustering - p. 221 25. Chapter 18 Big medical data mining system (BigMed) for the detection and classification of COVID-19 misinformation - p. 233 26. Chapter 19 Privacy security risks of big data processing in healthcare - p. 247 27. Chapter 20 Opportunities and challenges in healthcare with the management of big biomedical data - p. 265 28. Chapter 21 Future direction for healthcare based on big data analytics - p. 277 29. Chapter 22 Big data in orthopedics: between hypes and hopes - p. 291 30. Chapter 23 Predicting onset (type-2) of diabetes from medical records using binary class classification - p. 301 31. Chapter 24 Screening programs incorporating big data analytics - p. 313 32. Index - p. 329
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.
1. Big Data Analytics for Healthcare - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xiii 7. Preface - p. xvii 8. Chapter 1 Big data analytics in healthcare: theory, tools, techniques and its applications - p. 3 9. Chapter 2 Driving impact through big data utilization and analytics in the context of a Learning Health System - p. 13 10. Chapter 3 Classification of medical big data: a review of systematic analysis of medical big data in real-time setup - p. 23 11. Chapter 4 Towards big data framework in government public open data (GPOD) for health - p. 33 12. Chapter 5 Big data analytics techniques for healthcare - p. 49 13. Chapter 6 Big data analytics in precision medicine - p. 63 14. Chapter 7 Recent advances in processing, interpreting, and managing biological data for therapeutic intervention of human infectious disease - p. 73 15. Chapter 8 Big data analytics for health: a comprehensive review of techniques and applications - p. 83 16. Chapter 9 Recent applications of data mining in medical diagnosis and prediction - p. 95 17. Chapter 10 Big medical data analytics for diagnosis - p. 111 18. Chapter 11 Big data analytics and radiomics to discover diagnostics on different cancer types - p. 125 19. Chapter 12 Big medical data, cloud computing, and artificial intelligence for improving diagnosis in healthcare - p. 139 20. Chapter 13 Use of artificial intelligence for predicting infectious disease - p. 153 21. Chapter 14 Hospital data analytics system for tracking and predicting obese patients' lifestyle habits - p. 165 22. Chapter 15 Predictions on diabetic patient datasets using big data analytics and machine learning techniques - p. 179 23. Chapter 16 Skin cancer prediction using big data analytics and AI techniques - p. 201 24. Chapter 17 COVID-19 fake news analytics from social media using topic modeling and clustering - p. 221 25. Chapter 18 Big medical data mining system (BigMed) for the detection and classification of COVID-19 misinformation - p. 233 26. Chapter 19 Privacy security risks of big data processing in healthcare - p. 247 27. Chapter 20 Opportunities and challenges in healthcare with the management of big biomedical data - p. 265 28. Chapter 21 Future direction for healthcare based on big data analytics - p. 277 29. Chapter 22 Big data in orthopedics: between hypes and hopes - p. 291 30. Chapter 23 Predicting onset (type-2) of diabetes from medical records using binary class classification - p. 301 31. Chapter 24 Screening programs incorporating big data analytics - p. 313 32. Index - p. 329
