Deep Learning for Healthcare Decision Making

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.