Next generation eHealth: applied data science, machine learning and extreme computational intelligence
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. This book provides useful therapeutic targets to improve diagnosis, therapies, and...
Gespeichert in:
| Beteilige Person: | |
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| Weitere beteiligte Personen: | , , , |
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
London
Elsevier, AP, Academic Press
[2025]
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| Schlagwörter: | |
| Zusammenfassung: | 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. This book 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, this 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 |
| Beschreibung: | 1. The challenges for the next generation digital health: The disruptive character of Artificial Intelligence; 2. Data governance in healthcare organizations; 3. Enhancing patient welfare through responsible and AI-driven healthcare innovation: Progress made in OECD countries and the case of Greece; 4. The economic feasibility of digital health and telerehabilitation; 5. Intelligent digital twins: Scenarios, promises, and challenges in medicine and public health; 6. Digital twin in cardiology: Navigating the digital landscape for education, global health, and preventive medicine; 7. Review of data-driven generative AI models for knowledge extraction from scientific literature in healthcare; 8. Approximate computing for energy-efficient processing of biosignals in ehealth care systems; 9. Linked open research information on semantic web: Challenges and opportunities for Research information management (RIM) User’s; 10. The need of E-health and literacy of cancer patients for Healthcare providers; Ruchika Kalra, Meena Gupta and Priya Sharma; 11. eHealth concern over fine particulate matter air pollution and brain tumors; 12. Wearable devices developed to support dementia detection, monitoring, and intervention; 13. How artificial intelligence affects the future of pharmacy practice?; 14. Designing robust and resilient data strategy in health clusters (HCs): Use case identification for efficiency and performance enhancement; 15. Digital health as a bold contribution to sustainable and social inclusive development |
| Umfang: | xxxii, 305 Seiten Illustrationen, Diagramme 450 gr |
| ISBN: | 9780443136191 |
Internformat
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| 520 | |a 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. This book 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, this 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 | ||
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| spelling | Next generation eHealth applied data science, machine learning and extreme computational intelligence edited by Miltiadis D. Lytas, Abdulrahman Housawi, Basim S. Alsaywid, Naif Radi Aljohani London Elsevier, AP, Academic Press [2025] xxxii, 305 Seiten Illustrationen, Diagramme 450 gr txt rdacontent n rdamedia nc rdacarrier 1. The challenges for the next generation digital health: The disruptive character of Artificial Intelligence; 2. Data governance in healthcare organizations; 3. Enhancing patient welfare through responsible and AI-driven healthcare innovation: Progress made in OECD countries and the case of Greece; 4. The economic feasibility of digital health and telerehabilitation; 5. Intelligent digital twins: Scenarios, promises, and challenges in medicine and public health; 6. Digital twin in cardiology: Navigating the digital landscape for education, global health, and preventive medicine; 7. Review of data-driven generative AI models for knowledge extraction from scientific literature in healthcare; 8. Approximate computing for energy-efficient processing of biosignals in ehealth care systems; 9. Linked open research information on semantic web: Challenges and opportunities for Research information management (RIM) User’s; 10. The need of E-health and literacy of cancer patients for Healthcare providers; Ruchika Kalra, Meena Gupta and Priya Sharma; 11. eHealth concern over fine particulate matter air pollution and brain tumors; 12. Wearable devices developed to support dementia detection, monitoring, and intervention; 13. How artificial intelligence affects the future of pharmacy practice?; 14. Designing robust and resilient data strategy in health clusters (HCs): Use case identification for efficiency and performance enhancement; 15. Digital health as a bold contribution to sustainable and social inclusive development 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. This book 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, this 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 bisacsh Soft Computing (DE-588)4455833-8 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf E-Health (DE-588)7542254-2 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf E-Health (DE-588)7542254-2 s Maschinelles Lernen (DE-588)4193754-5 s Soft Computing (DE-588)4455833-8 s Data Science (DE-588)1140936166 s DE-604 Lytras, Miltiadēs 1973- (DE-588)136264034 edt Housawi, Abdulrahman edt Alsaywid, Basim edt Aljohani, Naif Radi 1983- (DE-588)1156855608 edt |
| spellingShingle | Lytras, Miltiadis Next generation eHealth applied data science, machine learning and extreme computational intelligence bisacsh Soft Computing (DE-588)4455833-8 gnd Data Science (DE-588)1140936166 gnd E-Health (DE-588)7542254-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4455833-8 (DE-588)1140936166 (DE-588)7542254-2 (DE-588)4193754-5 |
| title | Next generation eHealth applied data science, machine learning and extreme computational intelligence |
| title_auth | Next generation eHealth applied data science, machine learning and extreme computational intelligence |
| title_exact_search | Next generation eHealth applied data science, machine learning and extreme computational intelligence |
| title_full | Next generation eHealth applied data science, machine learning and extreme computational intelligence edited by Miltiadis D. Lytas, Abdulrahman Housawi, Basim S. Alsaywid, Naif Radi Aljohani |
| title_fullStr | Next generation eHealth applied data science, machine learning and extreme computational intelligence edited by Miltiadis D. Lytas, Abdulrahman Housawi, Basim S. Alsaywid, Naif Radi Aljohani |
| title_full_unstemmed | Next generation eHealth applied data science, machine learning and extreme computational intelligence edited by Miltiadis D. Lytas, Abdulrahman Housawi, Basim S. Alsaywid, Naif Radi Aljohani |
| title_short | Next generation eHealth |
| title_sort | next generation ehealth applied data science machine learning and extreme computational intelligence |
| title_sub | applied data science, machine learning and extreme computational intelligence |
| topic | bisacsh Soft Computing (DE-588)4455833-8 gnd Data Science (DE-588)1140936166 gnd E-Health (DE-588)7542254-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | bisacsh Soft Computing Data Science E-Health Maschinelles Lernen |
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