图书简介
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 learning models are neural networks of many layers, which can extract multiple levels of features from raw data. Deep learning applied to healthcare is a natural and promising direction with many initial successes. The authors cover deep neural networks, convolutional neural networks, recurrent neural networks, embedding methods, autoencoders, attention models, graph neural networks, memory networks, and generative models. It’s presented with concrete healthcare case studies such as clinical predictive modeling, readmission prediction, phenotyping, x-ray classification, ECG diagnosis, sleep monitoring, automatic diagnosis coding from clinical notes, automatic deidentification, medication recommendation, drug discovery (drug property prediction and molecule generation), and clinical trial matching.This textbook targets graduate-level students focused on deep learning methods and their healthcare applications. It can be used for the concepts of deep learning and its applications as well. Researchers working in this field will also find this book to be extremely useful and valuable for their research.
I Introduction.- I.1 Who should read this book?.- I.2 Book organization.- II Health Data.- II.1 The growth of EHR Adoption.- II.2 Health Data.- II.2.1 Life cycle of health data.- II.2.2 Structured Health Data.- II.2.3 Unstructured clinical notes.- II.2.4 Continuous signals.- II.2.5 Medical Imaging Data.- II.2.6 Biomedical data for in silico drug Discovery .- II.3 Health Data Standards.- III Machine Learning Basics.- III.1 Supervised Learning.- III.1.1 Logistic Regression.- III.1.2 Softmax Regression.- III.1.3 Gradient Descent.- III.1.4 Stochastic and Minibatch Gradient Descent.- III.2 Unsupervised Learning.- III.2.1 Principal component analysis.- III.2.2 t-distributed stochastic neighbor embedding (t-SNE).- III.2.3 Clustering.- III.3 Assessing Model Performance.- III.3.1 Evaluation Metrics for Regression Tasks.- III.3.2 Evaluation Metrics for Classification Tasks.- III.3.3 Evaluation Metrics for Clustering Tasks.- III.3.4 Evaluation Strategy.- III.4 Modeling Exercise.- III.5 Hands-On Practice.- 3.- 4 CONTENTS.- IVDeep Neural Networks (DNN).- IV.1 A Single neuron.- IV.1.1 Activation function.- IV.1.2 Loss Function.- IV.1.3 Train a single neuron.- IV.2 Multilayer Neural Network.- IV.2.1 Network Representation.- IV.2.2 Train a Multilayer Neural Network.- IV.2.3 Summary of the Backpropagation Algorithm.- IV.2.4 Parameters and Hyper-parameters.- IV.3 Readmission Prediction from EHR Data with DNN.- IV.4 DNN for Drug Property Prediction.- V Embedding.- V.1 Overview.- V.2 Word2Vec.- V.2.1 Idea and Formulation of Word2Vec.- V.2.2 Healthcare application of Word2Vec.- V.3 Med2Vec: two-level embedding for EHR.- V.3.1 Med2Vec Method.- V.4 MiME: Embed Internal Structure.- V.4.1 Notations of MIME.- V.4.2 Description of MIME.- V.4.3 Experiment results of MIME.- VI Convolutional Neural Networks (CNN).- VI.1 CNN intuition.- VI.2 Architecture of CNN.- VI.2.1 Convolution layer - 1D.- VI.2.2 Convolution layer - 2D.- VI.2.3 Pooling Layer.- VI.2.4 Fully Connected Layer.- VI.3 Backpropagation Algorithm in CNN*.- VI.3.1 Forward and Backward Computation for 1-D Data.- VI.3.2 Forward Computation and Backpropagation for 2-D Convolution.- Layer . .- VI.3.3 Special CNN Architecture.- VI.4 Healthcare Applications .- VI.5 Automated surveillance of cranial images for acute neurologic events.- VI.6 Detection of Lymph Node Metastases from Pathology Images.- VI.7 Cardiologist-level arrhythmia detection and classification in ambulatory.- ECG.- CONTENTS 5.- VIIRecurrent Neural Networks (RNN).- VII.1Basic Concepts and Notations.- VII.2Backpropagation Through Time (BPTT) algorithm.- VII.2.1Forward Pass.- VII.2.2 Backward Pass.- VII.3RNN Variants.- VII.3.1 Long Short-Term Memory (LSTM).- VII.3.2 Gated Recurrent Unit (GRU).- VII.3.3 Bidirectional RNN.- VII.3.4 Encoder-Decoder Sequence-to-Sequence Models.- VII.4Case Study: Early detection of heart failure.- VII.5Case Study: Sequential clinical event prediction.- VII.6Case Study: De-identification of Clinical Notes.- VII.7Case Study: Automatic Detection of Heart Disease from electrocardiography.- (ECG) Data.- VIIAIutoencoders (AE).- VIII.1Overview.- VIII.2Autoencoders.- VIII.3Sparse Autoencoders.- VIII.4Stacked Autoencoders.- VIII.5Denoising Autoencoders.- VIII.6Case Study: “Deep Patient” via stacked denoising autoencoders.- VIII.7Case Study: Learning from Noisy, Sparse, and Irregular Clinical.- data.- IX Attention Models.- IX.1 Overview.- IX.2 Attention Mechanism.- IX.2.1 Attention based on Encoder-Decoder RNN Models.- IX.2.2 Case Study: Attention Model over Longitudinal EHR.- IX.2.3 Case Study: Attention model over a Medical Ontology.- IX.2.4 Case Study: ICD Classification from Clinical Notes.- X Memory Networks.- X.1 Original Memory Networks.- X.2 End-to-end Memory Networks.- X.3 Case Study: Medication Recommendation.- X.4 EEG-RelNet: Memory Derived from Data.- X.5 Incorporate Memory from Unstructured Knowledge B
Trade Policy 买家须知
- 关于产品:
- ● 正版保障:本网站隶属于中国国际图书贸易集团公司,确保所有图书都是100%正版。
- ● 环保纸张:进口图书大多使用的都是环保轻型张,颜色偏黄,重量比较轻。
- ● 毛边版:即书翻页的地方,故意做成了参差不齐的样子,一般为精装版,更具收藏价值。
关于退换货:
- 由于预订产品的特殊性,采购订单正式发订后,买方不得无故取消全部或部分产品的订购。
- 由于进口图书的特殊性,发生以下情况的,请直接拒收货物,由快递返回:
- ● 外包装破损/发错货/少发货/图书外观破损/图书配件不全(例如:光盘等)
并请在工作日通过电话400-008-1110联系我们。
- 签收后,如发生以下情况,请在签收后的5个工作日内联系客服办理退换货:
- ● 缺页/错页/错印/脱线
关于发货时间:
- 一般情况下:
- ●【现货】 下单后48小时内由北京(库房)发出快递。
- ●【预订】【预售】下单后国外发货,到货时间预计5-8周左右,店铺默认中通快递,如需顺丰快递邮费到付。
- ● 需要开具发票的客户,发货时间可能在上述基础上再延后1-2个工作日(紧急发票需求,请联系010-68433105/3213);
- ● 如遇其他特殊原因,对发货时间有影响的,我们会第一时间在网站公告,敬请留意。
关于到货时间:
- 由于进口图书入境入库后,都是委托第三方快递发货,所以我们只能保证在规定时间内发出,但无法为您保证确切的到货时间。
- ● 主要城市一般2-4天
- ● 偏远地区一般4-7天
关于接听咨询电话的时间:
- 010-68433105/3213正常接听咨询电话的时间为:周一至周五上午8:30~下午5:00,周六、日及法定节假日休息,将无法接听来电,敬请谅解。
- 其它时间您也可以通过邮件联系我们:customer@readgo.cn,工作日会优先处理。
关于快递:
- ● 已付款订单:主要由中通、宅急送负责派送,订单进度查询请拨打010-68433105/3213。
本书暂无推荐
本书暂无推荐