Название: Deep Learning Techniques for Biomedical and Health Informatics Автор: Sujata Dash, Biswa Ranjan Acharya
Издательство: Springer
Год: 2020
Страниц: 395
Язык: английский
Формат: pdf (true)
Размер: 11.2 MB
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.
Deep learning methods with multiple levels of representation in which at each level the system learn higher abstract level representation. Deep learning based algorithms has demonstrated great performance to a variety of areas including computer vision, image processing, natural language processing, speech recognition, video analysis, biomedical and health informatics etc. Deep learning approaches such as neural networks such as deep belief network, convolutional neural network, deep auto-encoder, and deep generative networks have emerged as powerful computational models. These have shown significant success in dealing with massive data for large number of applications due to their capability to extract complex hidden features and learn efficient representation in unsupervised setting.
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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