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Next Generation eHealth Applied Data Science, Machine Learning and Extreme Computational Intelligence Next Generation Technology Driven Personalized Medicine And Smart Healthcare Series

Langue : Anglais
Couverture de l’ouvrage Next Generation eHealth
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. 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.

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.
Dedication
Preface
Acknowledgements
Editorial Advisory Board
Related Titles
About the Author

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9. Linked Open Research Information on Semantic Web: Challenges & Opportunities for RIM Users
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11. An M-health application to collect and analyze gestational diabetes data
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13. Exploring Brain Tumors with ResNet 50 Transfer Learning: A Case of Air Pollution
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16. Robust Artificial Intelligence and Machine Learning for Diseases Diagnosis
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17. The Data Strategy in the Madinah Health Cluster: Best Practices and Lessons Learnt from the application of Analytics Maturity Assessment
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18. Integration of Digital Health Services for Education and Research Skills capacity building at the Saudi National Institute of Health
19. Enhancing Patient Welfare through Responsible and AI-Driven Healthcare Innovation: Progress Made in OECD Countries and the Case of Greece
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Miltiadis D. Lytras is an expert in advanced computer science and management, editor, lecturer, and research consultant, with extensive experience in academia and the business sector in Europe and Asia. Dr. Lytras is a Research Professor at Deree College - The American College of Greece and a Distinguished Scientist at the King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia. Dr. Lytras is a world-class expert in the fields of cognitive computing, information systems, technology enabled innovation, social networks, computers in human behavior, and knowledge management. In his work, Dr. Lytras seeks to bring together and exploit synergies among scholars and experts committed to enhancing the quality of education for all.
Dr. Abdulrahman Housawi is a nephrologist and specialist in multi-organ transplant surgery and Chairman of the Multi-organ Transplant Research Committee at King Fahd Specialist Hospital, Dammam, KSA. He received his medical degree from the King Abdulaziz University in Jeddah, Saudi Arabia, his Master of Science degree with a focus on epidemiology and biostatistics from the University of Western Ontario, London, Canada, and a Master’s of Science in Health Administration from the University of Alabama-Birmingham. His research interests include the epidemiology of chronic kidney disease, developing research registries for CKD and solid organ transplants, the outcomes of living kidney donation and the long-term outcomes of kidney transplantation. From the PH-LEADER workshops he hopes to further his knowledge of transplants and outside aspects of surgery and its effects on the donors and their families. Currently, he is responsible for the development and implementation of the Saudi Commission’s strategy, including its transformation to a data-driven organization (2016–present)

Basim Alsaywid, Pediatric Urology Surgeon, graduated from King Abdulaziz University then completed Saudi Board of Urology in 2007. Obtained his Pediatric Urology Tr

  • Allows medical scientists, computer science experts, researchers, and health professionals to better educate themselves on Machine Learning practices and applications, and to benefit from the improvement of their knowledge skills
  • Presents various tested and current techniques of health literacy as a determinant of health and well-being
  • Provides insights into international research successfully implemented in patient care and education through the proper training of health professionals
  • Offers detailed guidance for diverse communities on their need to get timely, trusted, and integrated knowledge for the adoption of ML in healthcare processes and decisions

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