Introduction to Deep Learning for Healthcare

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 Base.- XIGraph Neural Networks.- XI.1 Overview.- XI.2 Graph Convolutional Networks.- XI.2.1 Basic Setting of GCN.- XI.2.2 Spatial Convolution on Graphs.- 6 CONTENTS.- XI.2.3 Spectral Convolution on Graphs.- XI.2.4 Approximate Graph Convolution.- XI.2.5 Neighborhood Aggregation.- XI.3 Neural Fingerprinting: Drug Molecule Embedding with GCN.- XI.4 Decagon: Modeling Polypharmacy Side Effects with GCN.- XI.5 Case Study: Multiview Drug-drug Interaction Prediction.- XIIGenerative Models.- XII.1Generative adversarial networks (GAN).- XII.1.1 The GAN Framework.- XII.1.2 The Cost Function of Discriminator.- XII.1.3 The Cost Function of Generator.- XII.2Variational Autoencoders (VAE).- XII.2.1 Latent Variable Models.- XII.2.2Objective Formulation.- XII.2.3Objective Approximation.- XII.2.4 Reparameterization Trick.- XII.3Case Study: Generating Patient Records.- XII.4Case Study: Small Molecule Generation for Drug Discovery.- XII CIonclusion.- XIII.1Model Setup.- XIII.2Model Training.- XIII.3Testing and Performance Evaluation.- XIII.4Result Visualization.- XIII.5Case Studies.- XIVAppendix.- XIV.1Regularization*.- XIV.1.1Vanishing or Exploding Gradient Problem.- XIV.1.2Dropout.- XIV.1.3Batch normalization.- XIV.2Stochastic Gradient Descent and Minibatch gradient descent*.- XIV.3Advanced optimization*.- XIV.3.1Momentum.- XIV.3.2Adagrad, Adadelta, and RMSprop.- XIV.3.3Adam.-

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• Aprendizagem mecânica
• Aspectos sanitários e de segurança da computação

Deep learning;healthcare applications;deep neural networks;Clinical predictive model;x-ray classification;clinical natural language processing;modeling clinical notes;EEG;ECG;drug discovery;convolutional neural networks;recurrent neural networks;embedding methods;autoencoder;attention models;graph neural networks;memory networks;generative models