Introduction to Deep Learning for Healthcare

ISBN-13: 9783030821869 / Angielski / Miękka / 2022

Cao Xiao;Jimeng Sun

Opis

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.

Spis treści

Contents

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

O autorze

Dr. Cao ``Danica'' Xiao is the senior director, head of data science and machine learning at Amplitude. Before that, she was the director of machine learning in the analytics center of excellence of IQVIA. Before IQVIA, she was a research staff member in IBM Research. Her work focuses on developing machine learning and deep learning models to solve real-world healthcare challenges. She got her Ph.D. degree from University of Washington, Seattle.

Dr. Jimeng Sun is a Health Innovation Professor at the Computer Science Department and Carle's Illinois College of Medicine in the University of Illinois Urbana-Champaign. His research focuses on artificial intelligence (AI) for healthcare, including deep learning for drug discovery, clinical trial optimization, computational phenotyping, clinical predictive modeling, treatment recommendation, and health monitoring. He completed his B.S. and M.Phil. in computer science at Hong Kong University of Science and Technology and his Ph.D. in computer science at Carnegie Mellon University.