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Deep learning techniques for biomedi...

Dash, Sujata.

Deep learning techniques for biomedical and health informatics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning techniques for biomedical and health informaticsedited by Sujata Dash ... [et al.].
其他作者:	Dash, Sujata.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xxv, 383 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-3-030-33966-1
ISBN:	9783030339661$q(electronic bk.)

Deep learning techniques for biomedical and health informatics
Deep learning techniques for biomedical and health informatics[electronic resource] /edited by Sujata Dash ... [et al.]. - Cham :Springer International Publishing :2020. - xxv, 383 p. :ill., digital ;24 cm. - Studies in big data,v.682197-6503 ;. - Studies in big data ;v.1..

MedNLU: Natural Language Understander for Medical Texts -- Deep Learning Based Biomedical Named Entity Recognition Systems -- Disambiguation Model for Bio-Medical Named Entity Recognition -- Applications of Deep Learning in Healthcare and Biomedicine -- Deep Learning for Clinical Decision Support Systems: A Review from the Panorama of Smart Healthcare -- Review of Machine Learning and Deep Learning based Recommender Systems for Health Informatics -- Deep Learning and Explainable AI in Healthcare using EHR -- Deep Learning for Analysis of Electronic Heath Records -- Bioinformatics Using Deep Architecture -- Intelligent, Secure Big Health Data Management using Deep Learning and Blockchain Technology: An Overview -- Malaria Disease Detection using CNN Technique with SGD, RMSprop and ADAM Optimizers -- Deep Reinforcement Learning based Personalized Health Recommendations.

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.

ISBN: 9783030339661$q(electronic bk.)

Standard No.: 10.1007/978-3-030-33966-1doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .D447 2020

Dewey Class. No.: 006.31

Deep learning techniques for biomedical and health informatics
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520 $a 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.
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