Artificial Intelligence and Big Data Analytics for Smart Healthcare serves as a key reference for practitioners and experts involved in healthcare as they strive to enhance the value added of healthcare and develop more sustainable healthcare systems. It brings together insights from emerging sophisticated information and communication technologies such as big data analytics, artificial intelligence, machine learning, data science, medical intelligence, and, by dwelling on their current and prospective applications, highlights managerial and policymaking challenges they may generate.
The book is split into five sections: big data infrastructure, framework and design for smart healthcare; signal processing techniques for smart healthcare applications; business analytics (descriptive, diagnostic, predictive and prescriptive) for smart healthcare; emerging tools and techniques for smart healthcare; and challenges (security, privacy, and policy) in big data for smart healthcare. The content is carefully developed to be understandable to different members of healthcare chain to leverage collaborations with researchers and industry.
1. Artificial Intelligence and Big Data Analytics for Smart Healthcare - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Dedication - p. v 6. Table of Contents - p. vii 7. List of contributors - p. xiii 8. Preface: artificial intelligence and big data analytics for smart healthcare: a digital transformation of healthcare primer - p. xv 9. Acknowledgments - p. xxix 10. Chapter 1 Healthcare in the times of artificial intelligence: setting a value-based context - p. 1 11. Chapter 2 High-level strategy for implementing artificial intelligence at the Saudi Commission for Health Specialties - p. 11 12. Chapter 3 Big data infrastructure: data mining, text mining, and citation context analysis in scientific literature - p. 25 13. Chapter 4 Place attachment theories: a spatial approach to smart health and healing - p. 47 14. Chapter 5 Utilizing IoT-based sensors and prediction model for health-care monitoring system - p. 63 15. Chapter 6 QoS of mobile cloud computing applications in healthcare - p. 81 16. Chapter 7 Analysis of Parkinson’s disease based on mobile application - p. 97 17. Chapter 8 Mobile Partogram—m-Health technology in the promotion of parturient’s health in the delivery room - p. 121 18. Chapter 9 Self-evaluation mobile application on mild cognitive impairment based on Mini–Mental State Examination with bilingual support - p. 135 19. Chapter 10 Spatiotemporal Big Data-Driven Vessel Traffic Risk Estimation for Promoting Maritime Healthcare: Lessons Learnt from Another Domain than Healthcare - p. 145 20. Chapter 11 Neurofeedback using video games for attention-deficit/hyperactivity disorder - p. 161 21. Chapter 12 Medical diagnosis in Alzheimer’s disease based on supervised and semisupervised learning - p. 177 22. Chapter 13 A support vector machine–based voice disorders detection using human voice signal - p. 197 23. Chapter 14 COVID-19 detection from X-ray images using artificial intelligence - p. 209 24. Chapter 15 Empowering the One Health approach and health resilience with digital technologies across OECD countries: the case of COVID-19 pandemic - p. 225 25. Chapter 16 An overview of artificial intelligence and big data analytics for smart healthcare: requirements, applications, and challenges - p. 243 26. Index - p. 255
Artificial Intelligence and Big Data Analytics for Smart Healthcare serves as a key reference for practitioners and experts involved in healthcare as they strive to enhance the value added of healthcare and develop more sustainable healthcare systems. It brings together insights from emerging sophisticated information and communication technologies such as big data analytics, artificial intelligence, machine learning, data science, medical intelligence, and, by dwelling on their current and prospective applications, highlights managerial and policymaking challenges they may generate.
The book is split into five sections: big data infrastructure, framework and design for smart healthcare; signal processing techniques for smart healthcare applications; business analytics (descriptive, diagnostic, predictive and prescriptive) for smart healthcare; emerging tools and techniques for smart healthcare; and challenges (security, privacy, and policy) in big data for smart healthcare. The content is carefully developed to be understandable to different members of healthcare chain to leverage collaborations with researchers and industry.
1. Artificial Intelligence and Big Data Analytics for Smart Healthcare - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Dedication - p. v 6. Table of Contents - p. vii 7. List of contributors - p. xiii 8. Preface: artificial intelligence and big data analytics for smart healthcare: a digital transformation of healthcare primer - p. xv 9. Acknowledgments - p. xxix 10. Chapter 1 Healthcare in the times of artificial intelligence: setting a value-based context - p. 1 11. Chapter 2 High-level strategy for implementing artificial intelligence at the Saudi Commission for Health Specialties - p. 11 12. Chapter 3 Big data infrastructure: data mining, text mining, and citation context analysis in scientific literature - p. 25 13. Chapter 4 Place attachment theories: a spatial approach to smart health and healing - p. 47 14. Chapter 5 Utilizing IoT-based sensors and prediction model for health-care monitoring system - p. 63 15. Chapter 6 QoS of mobile cloud computing applications in healthcare - p. 81 16. Chapter 7 Analysis of Parkinson’s disease based on mobile application - p. 97 17. Chapter 8 Mobile Partogram—m-Health technology in the promotion of parturient’s health in the delivery room - p. 121 18. Chapter 9 Self-evaluation mobile application on mild cognitive impairment based on Mini–Mental State Examination with bilingual support - p. 135 19. Chapter 10 Spatiotemporal Big Data-Driven Vessel Traffic Risk Estimation for Promoting Maritime Healthcare: Lessons Learnt from Another Domain than Healthcare - p. 145 20. Chapter 11 Neurofeedback using video games for attention-deficit/hyperactivity disorder - p. 161 21. Chapter 12 Medical diagnosis in Alzheimer’s disease based on supervised and semisupervised learning - p. 177 22. Chapter 13 A support vector machine–based voice disorders detection using human voice signal - p. 197 23. Chapter 14 COVID-19 detection from X-ray images using artificial intelligence - p. 209 24. Chapter 15 Empowering the One Health approach and health resilience with digital technologies across OECD countries: the case of COVID-19 pandemic - p. 225 25. Chapter 16 An overview of artificial intelligence and big data analytics for smart healthcare: requirements, applications, and challenges - p. 243 26. Index - p. 255
