Titlebook: Deep Learning Techniques for Biomedical and Health Informatics; Sujata Dash,Biswa Ranjan Acharya,Arpad Kelemen Book 2020 Springer Nature S

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2197-6503 irect 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 t978-3-030-33968-5978-3-030-33966-1Series ISSN 2197-6503 Series E-ISSN 2197-6511

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Deep Learning Based Biomedical Named Entity Recognition Systemscommunication. The various varieties of named entities includes person name, association name, place name, numbers etc. During this book chapter we tend to area unit solely handling medicine named entity recognition (Bio-NER) that could be a basic assignment within the conducting of medicine text te

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Disambiguation Model for Bio-Medical Named Entity Recognition approach i.e. Bidirectional Long Short Term Memory (Bi-LSTM), It mistakenly labeled a gene entity “BRCA1” as a disease entity which is “BRCA1 abnormality” or “Braca1-deficient” present in the training dataset. Similarly, “VHL (Von Hippel-Lindau disease),” which is one of the genes named labeled as

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Applications of Deep Learning in Healthcare and Biomedicines. It is an Artificial Neural Network that designs models computationally that are composed of many processing layers, in order to learn data representations with numerous levels of abstraction. Research suggests that deep learning might have benefits over previous algorithms of machine learning and

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Deep Learning for Clinical Decision Support Systems: A Review from the Panorama of Smart Healthcareng the quality of clinical healthcare enormously. Such kind of intelligent decision making in healthcare and clinical practice is also expected to result in holistic treatment. In this chapter, we review and accumulate various existing DL techniques and their applications for decision support in cli

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Deep Reinforcement Learning Based Personalized Health Recommendationsm that consists of exercises and preferable sports. We try to exploit an “Actor-Critic” model for enhancing the ability of the model to continuously update information seeking strategies based on user’s real-time feedback. Health industry usually deals with long-term issues. Traditional recommender