Deep Learning Techniques for Biomedical and Health Informatics (1st ed. 2020) (Studies in Big Data #68)

Synopsis

This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim of healthcare informatics is to ensure high-quality, efficient health care, and better treatment and quality of life by efficiently analyzing abundant biomedical and healthcare data, including patient data and electronic health records (EHRs), as well as lifestyle problems. In the past, it was common to have a domain expert to develop a model for biomedical or health care applications; however, recent advances in the representation of learning algorithms (deep learning techniques) make it possible to automatically recognize the patterns and represent the given data for the development of such model. This book allows new researchers and practitioners working in the field to quickly understand the best-performing methods. It also enables them to compare different approaches and carry forward their research in an important area that has a direct impact on improving the human life and health. It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in the fields of machine learning, deep learning, biomedical engineering, health informatics, and related fields.

Copyright:

2020

Book Details

Book Quality:

Publisher Quality

ISBN-13:

9783030339661

Related ISBNs:

9783030339654

Publisher:

Springer International Publishing

Date of Addition:

11/23/19

Copyrighted By:

Springer

Adult content:

No

Language:

English

Has Image Descriptions:

No

Categories:

Nonfiction, Science, Computers and Internet, Technology, Medicine

Submitted By:

Bookshare Staff

Usage Restrictions:

This is a copyrighted book.

Edited by:

Sujata Dash

Edited by:

Biswa Ranjan Acharya

Edited by:

Mamta Mittal

Edited by:

Ajith Abraham

Edited by:

Arpad Kelemen