Next Generation eHealth: Applied Data Science, Machine Learning and Extreme Computational Intelligence discusses the emergence, the impact, and the potential of sophisticated computational capabilities in healthcare. The title provides useful therapeutic targets to improve diagnosis, therapies and prognosis of diseases as well as helping with the establishment of better and more efficient next generation medicine and medical systems.Machine Learning as a field greatly contributes to next generation medical research with the goal of improving Medicine practices and Medical Systems. As a contributing factor to better health outcomes the book highlights the need for advanced training of professionals from various health areas, clinicians, educators, and social professionals who deal with patients. Content illustrates current issues and future promises as they pertain to all stakeholders, including informaticians, professionals in diagnostics, key industry experts in biotech, pharma, administrators, clinicians, patients, educators, students, health professionals, social scientists and legislators, health providers, advocacy groups, and more.With a focus on Machine Learning, Deep learning and Neural Networks this volume communicates in an integrated, fresh, and novel way the impact of Data Science and Computational Intelligence to diverse audiences.
1. Introduction - p. Page ix 2. List of Illustrations - p. Page xix 3. List of Tables - p. Page xx 4. Chapter 1 : The challenges for the next generation digital health: The disruptive character of Artificial Intelligence - p. Page xx 5. Chapter 2 : Data governance in healthcare organizations - p. Page 10 6. Chapter 3 : Enhancing patient welfare through responsible and AI-driven healthcare innovation: Progress made in OECD countries and the case of Greece - p. Page 30 7. Chapter 4 : The economic feasibility of digital health and telerehabilitation - p. Page 64 8. Chapter 5 : Intelligent digital twins: Scenarios, promises, and challenges in medicine and public health - p. Page 90 9. Chapter 6 : Digital twin in cardiology: Navigating the digital landscape for education, global health, and preventive medicine - p. Page 107 10. Chapter 7 : Review of data-driven generative AI models for knowledge extraction from scientific literature in healthcare - p. Page 125 11. Chapter 8 : Approximate computing for energy-efficient processing of biosignals in ehealth care systems - p. Page 143 12. Chapter 9 : Linked open research information on semantic web: Challenges and opportunities for Research information management (RIM) User's - p. Page 160 13. Chapter 10 : The need of E-health and literacy of cancer patients for Healthcare providers - p. Page 177 14. Chapter 11 : eHealth concern over fine particulate matter air pollution and brain tumors - p. Page 194 15. Chapter 12 : Wearable devices developed to support dementia detection, monitoring, and intervention - p. Page 218 16. Chapter 13 : How artificial intelligence affects the future of pharmacy practice? - p. Page 244 17. Chapter 14 : Designing robust and resilient data strategy in health clusters (HCs): Use case identification for efficiency and performance enhancement - p. Page 258 18. Chapter 15 : Digital health as a bold contribution to sustainable and social inclusive development - p. Page 286 19. Index - p. Page 297
Next Generation eHealth: Applied Data Science, Machine Learning and Extreme Computational Intelligence discusses the emergence, the impact, and the potential of sophisticated computational capabilities in healthcare. The title provides useful therapeutic targets to improve diagnosis, therapies and prognosis of diseases as well as helping with the establishment of better and more efficient next generation medicine and medical systems.Machine Learning as a field greatly contributes to next generation medical research with the goal of improving Medicine practices and Medical Systems. As a contributing factor to better health outcomes the book highlights the need for advanced training of professionals from various health areas, clinicians, educators, and social professionals who deal with patients. Content illustrates current issues and future promises as they pertain to all stakeholders, including informaticians, professionals in diagnostics, key industry experts in biotech, pharma, administrators, clinicians, patients, educators, students, health professionals, social scientists and legislators, health providers, advocacy groups, and more.With a focus on Machine Learning, Deep learning and Neural Networks this volume communicates in an integrated, fresh, and novel way the impact of Data Science and Computational Intelligence to diverse audiences.
1. Introduction - p. Page ix 2. List of Illustrations - p. Page xix 3. List of Tables - p. Page xx 4. Chapter 1 : The challenges for the next generation digital health: The disruptive character of Artificial Intelligence - p. Page xx 5. Chapter 2 : Data governance in healthcare organizations - p. Page 10 6. Chapter 3 : Enhancing patient welfare through responsible and AI-driven healthcare innovation: Progress made in OECD countries and the case of Greece - p. Page 30 7. Chapter 4 : The economic feasibility of digital health and telerehabilitation - p. Page 64 8. Chapter 5 : Intelligent digital twins: Scenarios, promises, and challenges in medicine and public health - p. Page 90 9. Chapter 6 : Digital twin in cardiology: Navigating the digital landscape for education, global health, and preventive medicine - p. Page 107 10. Chapter 7 : Review of data-driven generative AI models for knowledge extraction from scientific literature in healthcare - p. Page 125 11. Chapter 8 : Approximate computing for energy-efficient processing of biosignals in ehealth care systems - p. Page 143 12. Chapter 9 : Linked open research information on semantic web: Challenges and opportunities for Research information management (RIM) User's - p. Page 160 13. Chapter 10 : The need of E-health and literacy of cancer patients for Healthcare providers - p. Page 177 14. Chapter 11 : eHealth concern over fine particulate matter air pollution and brain tumors - p. Page 194 15. Chapter 12 : Wearable devices developed to support dementia detection, monitoring, and intervention - p. Page 218 16. Chapter 13 : How artificial intelligence affects the future of pharmacy practice? - p. Page 244 17. Chapter 14 : Designing robust and resilient data strategy in health clusters (HCs): Use case identification for efficiency and performance enhancement - p. Page 258 18. Chapter 15 : Digital health as a bold contribution to sustainable and social inclusive development - p. Page 286 19. Index - p. Page 297
