Introduction to Deep Learning for Healthcare

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...

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Bibliographic Details
Main Authors: Xiao, Cao, Sun, Jimeng (Author)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2021, 2021
Edition:1st ed. 2021
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • 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
  • 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
  • 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.-
  • 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
  • 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
  • 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