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. 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. 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 skillsProvides various tested and current techniques of health literacy as a determinant of health and wellbeingProvides insight into international research successfully implemented in patient care and education through the proper training of health professionalsOffers 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 INDICE: 1. The Challenges for the Next Generation Digital Health: The disruptive character of Artificial Intelligence and Machine Learning2. Data Governance in Health Clusters: Applying data strategy for accountable healthcare3. Intelligent digital twins: Scenarios, promises and challenges in medicine and public health4. Approximate Computing for Energy-Efficient Processing of Bio-signals in e-Health Care Systems5. A smart Artificial intelligence and IoT based system for monitoring of COVID19 using chest X-ray images6. Review of Data-Driven Generative AI Models for Knowledge Extraction from Scientific Literature in Healthcare7. Machine Learning for dynamic composition of Health Education materials8. The Digital Healthcare Ecosystem in United Arab Emirates9. Linked Open Research Information on Semantic Web: Challenges & Opportunities for RIM Users10. A Multi-objective Optimal Scheduling Patient Appointments Algorithm for Smart Healthcare11. An M-health application to collect and analyze gestational diabetes data12. E-Health and Cancer screening form scientific literature in healthcare13. Exploring Brain Tumors with ResNet 50 Transfer Learning: A Case of Air Pollution14. Wearable devices developed to support dementia detection, monitoring and intervention15. The Economic Feasibility of Digital Health and Telerehabilitation16. Robust Artificial Intelligence and Machine Learning for Diseases Diagnosis17. The Data Strategy in the Madinah Health Cluster: Best Practices and Lessons Learnt from the application of Analytics Maturity Assessment18. Integration of Digital Health Services for Education and Research Skills capacity building at the Saudi National Institute of Health19. Enhancing Patient Welfare through Responsible and AI-Driven Healthcare Innovation: Progress Made in OECD Countries and the Case of Greece20. Digital Health as a bold contribution to Sustainable and Social Inclusive Development

• ISBN: 978-0-443-13619-1
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 230
• Fecha Publicación: 01/10/2024
• Nº Volúmenes: 1
• Idioma: Inglés