Deep Learning Techniques for Biomedical and Health Informatics (Hardcover, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Big Data, 68
Release date:	November 2019
First published:	2020
Editors:	Sujata Dash • Biswa Ranjan Acharya • Mamta Mittal • Ajith Abraham • Arpad Kelemen
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	383
Edition:	1st ed. 2020
ISBN-13:	978-3-03-033965-4
Categories:	Books > Medicine > Nursing & ancillary services > Biomedical engineering > General Books > Computing & IT > Applications of computing > Databases > General Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	3-03-033965-3
Barcode:	9783030339654

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