Deep Learning Techniques for Biomedical and Health Informatics Books

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Deep Learning Techniques for Biomedical and Health Informatics


Deep Learning Techniques for Biomedical and Health Informatics
  • Author : Sujata Dash
  • Publisher : Springer
  • Release : 2019-11-25
  • ISBN : 3030339653
  • Language : En, Es, Fr & De
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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.

Handbook of Deep Learning in Biomedical Engineering and Health Informatics


Handbook of Deep Learning in Biomedical Engineering and Health Informatics
  • Author : E. Golden Julie
  • Publisher : CRC Press
  • Release : 2021-09-22
  • ISBN : 9781000370492
  • Language : En, Es, Fr & De
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This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease. This volume delves into a variety of applications, techniques, algorithms, platforms, and tools used in this area, such as image segmentation, classification, registration, and computer-aided analysis. The editors proceed on the principle that accurate diagnosis of disease depends on image acquisition and interpretation. There are many methods to get high resolution radiological images, but we are still lacking in automated image interpretation. Currently deep learning techniques are providing a feasible solution for automatic diagnosis of disease with good accuracy. Analyzing clinical data using deep learning techniques enables clinicians to diagnose diseases at an early stage and treat patients more effectively. Chapters explore such approaches as deep learning algorithms, convolutional neural networks and recurrent neural network architecture, image stitching techniques, deep RNN architectures, and more. This volume also depicts how deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as cancer, COVID-19, acute neurocutaneous syndrome, cardiovascular and neuro diseases, skin lesions and skin cancer, etc. Key features: Introduces important recent technological advancements in the field Describes the various techniques, platforms, and tools used in biomedical deep learning systems Includes informative case studies that help to explain the new technologies Handbook of Deep Learning in Biomedical Engineering and Health Informatics provides a thorough exploration of biomedical systems applied with deep learning techniques and will provide valuable information for researchers, medical and industry practitioners, academicians, and students.

Deep Learning Techniques for Biomedical and Health Informatics


Deep Learning Techniques for Biomedical and Health Informatics
  • Author : Sujata Dash
  • Publisher :
  • Release : 2020
  • ISBN : 303033967X
  • Language : En, Es, Fr & De
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"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"--Publisher's description.

Deep Learning in Biomedical and Health Informatics


Deep Learning in Biomedical and Health Informatics
  • Author : Taylor & Francis Group
  • Publisher : CRC Press
  • Release : 2021-08-20
  • ISBN : 0367726041
  • Language : En, Es, Fr & De
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This book provides a proficient guide on the relationship between AI and healthcare and how AI is changing all aspects of the health care industry. It also covers how deep learning will help in diagnosis and prediction of disease spread. The editors present a comprehensive review of research, applying deep learning in health informatics, in the fields of medical imaging, electronic health records, genomics, sensing, and also highlights various challenges in applying the deep learning in health care. The Book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments and applications of deep learning and to solve emerging problems in health care and domain. This book is intended for computer scientists, biomedical engineers, and health care professionals researching and developing deep learning techniques.

Deep Learning Techniques for Biomedical and Health Informatics


Deep Learning Techniques for Biomedical and Health Informatics
  • Author : Dr. Basant Agarwal
  • Publisher : Academic Press
  • Release : 2020-01-14
  • ISBN : 9780128190623
  • Language : En, Es, Fr & De
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Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis