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020 _a9783030339661
024 7 _a10.1007/978-3-030-33966-1
_2doi
040 _aES-MaUEC
_bspa
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_dES-MaUEC
041 0 _aeng
050 4 _aQ325.5
_b2020 EB
245 0 0 _aDeep Learning Techniques for Biomedical and Health Informatics
_cedited by Sujata Dash, Biswa Ranjan Acharya, Mamta Mittal, Ajith Abraham, Arpad Kelemen
250 _aFirst edition
264 1 _aCham
_bSpringer International Publishing :
_bImprint Springer
_c2020
300 _a1 recurso en línea (XXV, 383 páginas)
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
490 0 _aStudies in Big Data
_x2197-6503
_v68
490 0 _aIntelligent Technologies and Robotics (Springer-42732)
505 0 _aMedNLU: 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.
520 3 _aThis 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. .
988 _aPrimersemestre_2020_Robotics
650 7 _2embne
_aAprendizaje automático
_9166090
650 7 _2embne
_9421371
_aInteligencia artificial en medicina
700 1 _aDash, Sujata.
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_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aAcharya, Biswa Ranjan.
_eeditor
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700 1 _aMittal, Mamta.
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700 1 _aAbraham, Ajith
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700 1 _aKelemen, Arpad.
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773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9783030339678
776 0 8 _iPrinted edition:
_z9783030339685
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-33966-1
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
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