书目名称 | Introduction to Deep Learning for Healthcare | 编辑 | Cao Xiao,Jimeng Sun | 视频video | | 概述 | Introduces the concepts of deep learning models in the context of a specific application domain in healthcare.Presents the neural network models/algorithms and their concrete applications in healthcar | 图书封面 |  | 描述 | .This textbook presents deep learning models and their healthcare applications. It focuses on rich health data and deep learning models that can effectively model health data. Healthcare data: Among all healthcare technologies, electronic health records (EHRs) had vast adoption and a significant impact on healthcare delivery in recent years. One crucial benefit of EHRs is to capture all the patient encounters with rich multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for diagnoses and procedures, lab results, and medication information. Unstructured data contain 1) clinical notes as text, 2) medical imaging data such as X-rays, echocardiogram, and magnetic resonance imaging (MRI), and 3) time-series data such as the electrocardiogram (ECG) and electroencephalogram (EEG). Beyond the data collected during clinical visits, patient self-generated/reported data start to grow thanks to wearable sensors’increasing use. The authors present deep learning case studies on all data described..Deep learning models: Neural network models are a class of machine learning methods with a long history. Deep lear | 出版日期 | Textbook 2021 | 关键词 | Deep learning; healthcare applications; deep neural networks; Clinical predictive model; x-ray classific | 版次 | 1 | doi | https://doi.org/10.1007/978-3-030-82184-5 | isbn_softcover | 978-3-030-82186-9 | isbn_ebook | 978-3-030-82184-5 | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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