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Titlebook: Introduction to Deep Learning for Healthcare; Cao Xiao,Jimeng Sun Textbook 2021 The Editor(s) (if applicable) and The Author(s), under exc

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發(fā)表于 2025-3-21 17:00:04 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Introduction to Deep Learning for Healthcare
編輯Cao Xiao,Jimeng Sun
視頻videohttp://file.papertrans.cn/474/473605/473605.mp4
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
圖書封面Titlebook: Introduction to Deep Learning for Healthcare;  Cao Xiao,Jimeng Sun Textbook 2021 The Editor(s) (if applicable) and The Author(s), under exc
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
關(guān)鍵詞Deep learning; healthcare applications; deep neural networks; Clinical predictive model; x-ray classific
版次1
doihttps://doi.org/10.1007/978-3-030-82184-5
isbn_softcover978-3-030-82186-9
isbn_ebook978-3-030-82184-5
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Generative Models, examples. Generative models have several advantages over discriminative models. For example, they are more effective in modeling high-dimensional probability distributions, which are often seen in many application domains, including healthcare.
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Health Data,Health data are diverse with multiple modalities. This chapter will introduce different types of health data, including structured health data (e.g., diagnosis codes, procedure codes) and unstructured data (e.g., clinical notes, medical images). We will also present the popular health data standards for representing those data.
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Attention Models,Accuracy and interpretability are two desirable properties of successful predictive models. Most of deep learning models try to achieve high accuracy without much consideration of interpretability. The attention mechanism is one rare occasion that allows neural network models to achieve both accuracy and interpretability.
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