Medical Analytics for Clinical and Healthcare Applications

1. Auflage 2025
ISBN: 978-1-394-30145-4
Verlag: Wiley

Buch, Englisch, 352 Seiten

The book is essential for anyone exploring the forefront of healthcare innovation, as it offers a thorough exploration of transformative data-driven methodologies that can significantly enhance patient outcomes and clinical efficiency in today’s evolving medical landscape.

In today’s rapidly advancing healthcare landscape, the integration of medical analytics has become essential for improving patient outcomes, clinical efficiency, and decision-making. Medical Analytics for Clinical and Healthcare Applications provides a comprehensive examination of how data-driven methodologies are revolutionizing the medical field. This book offers a deep dive into innovative techniques, real-world applications, and emerging trends in medical analytics, showcasing how these advancements are transforming disease detection, diagnosis, treatment planning, and healthcare management.

Spanning sixteen chapters across five subsections, this edited volume covers a wide array of topics—from foundational principles of medical data analysis to cutting-edge applications in predictive healthcare and medical data security. Readers will encounter state-of-the-art methodologies, including machine learning models, predictive analytics, and deep learning techniques applied to various healthcare challenges such as mental health disorders, cancer detection, and hospital mortality predictions. Medical Analytics for Clinical and Healthcare Applications equips readers with the knowledge to harness the power of medical analytics and its potential to shape the future of healthcare. Through its interdisciplinary approach and expert insights, this volume is poised to serve as a valuable resource for advancing healthcare technologies and improving the overall quality of care.

Readers will find the volume: - Explores the latest medical analytics techniques applied across clinical settings, from diagnosis to treatment optimization;
- Features real-world case studies and tools for implementing data-driven solutions in healthcare;
- Bridges the gap between healthcare professionals, data scientists, and engineers for collaborative innovation in medical technologies;
- Provides foresight into emerging trends and technologies shaping the future of healthcare analytics.

Audience

Healthcare professionals, clinical researchers, medical data scientists, biomedical engineers, IT professionals, academics, and policymakers focused on the intersection of medicine and data analytics.

Autoren/Hrsg.

Kalita, Kanak

Gao, Xiao-Zhi

Zindani, Divya

Ganesh, Narayanan

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Bildgebende Verfahren, Nuklearmedizin, Strahlentherapie Radiologie, Bildgebende Verfahren

Über Autor(innen)

Kanak Kalita, PhD is an accomplished professor and researcher in the field of computational engineering with over eight years of experience. He has published over 180 articles in international journals and edited five books. His research interests include machine learning, fuzzy decision making, metamodeling, process optimization, finite element methods, and composites.

Divya Zindani, PhD is an assistant professor in Department of Mechanical Engineering at the Sri Sivasubramaniya Nadar College of Engineering. He has published 15 patents, 15 books, over 20 chapters, and more than 60 journal publications. His research interests include sustainable materials, optimization, decision support systems, and supply chain management.

Narayanan Ganesh, PhD is a senior associate professor in the School of Computer Science and Engineering at the Vellore Institute of Technology with over two decades of experience. He has over 35 publications to his credit, including internationally published journal articles and book chapters. His research interests include software engineering, agile software development, prediction and optimization techniques, deep learning, image processing, and data analytics.

Xiao-Zhi Gao, PhD is a professor at the University of Eastern Finland. He has published over 400 technical papers in international journals and conferences. His research focuses on nature-inspired computing methods with applications in optimization, data mining, machine learning, control, signal processing, and industrial electronics.