Deep Learning for Healthcare Decision Making

Deep Learning for Healthcare Decision Making

River Publishers Series in Biomedical Engineering

Deep Learning for Healthcare Decision Making

Vishal Jain, Sharda University, India
Jyotir Moy Chatterjee, Lord Buddha Education Foundation, Nepal
Ishaani Priyadarshini, UC Berkeley, USA
Fadi Al-Turjman, Near East University, Turkey

ISBN: 9788770223898 e-ISBN: 9788770223881

Available: February 2023

Health care today is known to suffer from siloed and fragmented data, delayed clinical communications, and disparate workflow tools due to the lack of interoperability caused by vendor-locked health care systems, lack of trust among data holders, and security/privacy concerns regarding data sharing. The health information industry is ready for big leaps and bounds in terms of growth and advancement.

This book is an attempt to unveil the hidden potential of the enormous amount of health information and technology. Throughout this book, we attempt to combine numerous compelling views, guidelines, and frameworks to enable personalized health care service options through the successful application of deep learning frameworks. The progress of the health-care sector will be incremental as it learns from associations between data over time through the application of suitable AI, deep net frameworks, and patterns. The major challenge health care is facing is the effective and accurate learning of unstructured clinical data through the application of precise algorithms. Incorrect input data leading to erroneous outputs with false positives is intolerable in healthcare as patients’ lives are at stake. This book is written with the intent to uncover the stakes and possibilities involved in realizing personalized health-care services through efficient and effective deep learning algorithms.

The specific focus of this book will be on the application of deep learning in any area of health care, including clinical trials, telemedicine, health records management, etc.
Healthcare, medicine & pharmaceuticals, deep learning, public healthcare management, healthcare IT management, health information management, medical research, clinical data exchange, electronic health record.