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.-.