Next Generation eHealth

Applied Data Science, Machine Learning and Extreme Computational Intelligence

Miltiadis Lytras, Abdulrahman Housawi, Basim Alsaywid, Naif Radi Aljohani

• Publisher: Academic Press
• Year: 2024
• Topic: Biological Sciences
• eBook ISBN:
  9780443136207
• Subtopic: Molecular Biology
• Index: Biological Sciences

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 skills - Provides various tested and current techniques of health literacy as a determinant of health and well-being - Provides insight 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. professionals involved with healthcare to leverage productive partnerships with technology developers

Table of contents

• Cover
• Front Matter
• Table of Contents
• Copyright
• Dedication
• Contributors
• About the editors
• Other titles by this editor
• Acknowledgments
• Introduction
• List of Illustrations
• List of Tables
• Chapter 1 : The challenges for the next generation digital health: The disruptive character of Artificial Intelligence
• Chapter 2 : Data governance in healthcare organizations
• Chapter 3 : Enhancing patient welfare through responsible and AI-driven healthcare innovation: Progress made in OECD countries and the case of Greece
• Chapter 4 : The economic feasibility of digital health and telerehabilitation
• Chapter 5 : Intelligent digital twins: Scenarios, promises, and challenges in medicine and public health
• Chapter 6 : Digital twin in cardiology: Navigating the digital landscape for education, global health, and preventive medicine
• Chapter 7 : Review of data-driven generative AI models for knowledge extraction from scientific literature in healthcare
• Chapter 8 : Approximate computing for energy-efficient processing of biosignals in ehealth care systems
• Chapter 9 : Linked open research information on semantic web: Challenges and opportunities for Research information management (RIM) User's
• Chapter 10 : The need of E-health and literacy of cancer patients for Healthcare providers
• Chapter 11 : eHealth concern over fine particulate matter air pollution and brain tumors
• Chapter 12 : Wearable devices developed to support dementia detection, monitoring, and intervention
• Chapter 13 : How artificial intelligence affects the future of pharmacy practice?
• Chapter 14 : Designing robust and resilient data strategy in health clusters (HCs): Use case identification for efficiency and performance enhancement
• Chapter 15 : Digital health as a bold contribution to sustainable and social inclusive development
• Index
• A