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...
Main Authors: | , |
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Format: | eBook |
Language: | English |
Published: |
Cham
Springer International Publishing
2021, 2021
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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