Healthcare Data Analytics and Management help readers disseminate cutting-edge research that delivers insights into the analytic tools, opportunities, novel strategies, techniques and challenges for handling big data, data analytics and management in healthcare. As the rapidly expanding and heterogeneous nature of healthcare data poses challenges for big data analytics, this book targets researchers and bioengineers from areas of machine learning, data mining, data management, and healthcare providers, along with clinical researchers and physicians who are interested in the management and analysis of healthcare data.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Contributors - p. xiii 6. Foreword - p. xvii 7. Preface - p. xix 8. Chapter 1: Internet of things, smart sensors, and pervasive systems: Enabling connected and pervasive healthcare - p. xx 9. Chapter 2: Migration of healthcare relational database to NoSQL cloud database for healthcare analytics and management - p. 58 10. Chapter 3: Developing a decision support system for big data analysis and cost allocation in national healthcare - p. 87 11. Chapter 4: Securing large datasets involving fast-performing key bunch matrix block cipher - p. 109 12. Chapter 5: Comparative analysis of semantic frameworks in healthcare - p. 132 13. Chapter 6: Smart ambulance system using concept of big data and internet of things - p. 154 14. Chapter 7: Mathematical methods of ECG data analysis - p. 176 15. Chapter 8: Smart information technology for universal healthcare - p. 209 16. Chapter 9: Handling uncertainty in IoT design: An approach of statistical machine learning with distributed second-order optimization - p. 226 17. Chapter 10: A reversible and secure electronic patient record embedding technique using histogram bin shifting and RC6 encryption - p. 243 18. Chapter 11: Secure and reversible data hiding scheme for healthcare system using magic rectangle and a new interpolation technique - p. 266 19. Index - p. 311
Healthcare Data Analytics and Management help readers disseminate cutting-edge research that delivers insights into the analytic tools, opportunities, novel strategies, techniques and challenges for handling big data, data analytics and management in healthcare. As the rapidly expanding and heterogeneous nature of healthcare data poses challenges for big data analytics, this book targets researchers and bioengineers from areas of machine learning, data mining, data management, and healthcare providers, along with clinical researchers and physicians who are interested in the management and analysis of healthcare data.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Contributors - p. xiii 6. Foreword - p. xvii 7. Preface - p. xix 8. Chapter 1: Internet of things, smart sensors, and pervasive systems: Enabling connected and pervasive healthcare - p. xx 9. Chapter 2: Migration of healthcare relational database to NoSQL cloud database for healthcare analytics and management - p. 58 10. Chapter 3: Developing a decision support system for big data analysis and cost allocation in national healthcare - p. 87 11. Chapter 4: Securing large datasets involving fast-performing key bunch matrix block cipher - p. 109 12. Chapter 5: Comparative analysis of semantic frameworks in healthcare - p. 132 13. Chapter 6: Smart ambulance system using concept of big data and internet of things - p. 154 14. Chapter 7: Mathematical methods of ECG data analysis - p. 176 15. Chapter 8: Smart information technology for universal healthcare - p. 209 16. Chapter 9: Handling uncertainty in IoT design: An approach of statistical machine learning with distributed second-order optimization - p. 226 17. Chapter 10: A reversible and secure electronic patient record embedding technique using histogram bin shifting and RC6 encryption - p. 243 18. Chapter 11: Secure and reversible data hiding scheme for healthcare system using magic rectangle and a new interpolation technique - p. 266 19. Index - p. 311
