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  • © 2021

Introduction to Deep Learning for Healthcare

  • Introduces the concepts of deep learning models in the context of a specific application domain in healthcare

  • Presents the neural network models/algorithms and their concrete applications in healthcare

  • Includes cases studies, exercises and examples

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  • ISBN: 978-3-030-82184-5
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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xi
  2. Introduction

    • Cao Xiao, Jimeng Sun
    Pages 1-8
  3. Health Data

    • Cao Xiao, Jimeng Sun
    Pages 9-22
  4. Machine Learning Basics

    • Cao Xiao, Jimeng Sun
    Pages 23-40
  5. Deep Neural Networks (DNN)

    • Cao Xiao, Jimeng Sun
    Pages 41-61
  6. Embedding

    • Cao Xiao, Jimeng Sun
    Pages 63-81
  7. Convolutional Neural Networks (CNN)

    • Cao Xiao, Jimeng Sun
    Pages 83-109
  8. Recurrent Neural Networks (RNN)

    • Cao Xiao, Jimeng Sun
    Pages 111-135
  9. Autoencoders (AE)

    • Cao Xiao, Jimeng Sun
    Pages 137-146
  10. Attention Models

    • Cao Xiao, Jimeng Sun
    Pages 147-161
  11. Graph Neural Networks

    • Cao Xiao, Jimeng Sun
    Pages 163-179
  12. Memory Networks

    • Cao Xiao, Jimeng Sun
    Pages 181-203
  13. Generative Models

    • Cao Xiao, Jimeng Sun
    Pages 205-222
  14. Back Matter

    Pages 223-232

About this book

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.

Keywords

  • 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

Authors and Affiliations

  • Seattle, USA

    Cao Xiao

  • San Francisco, USA

    Jimeng Sun

About the authors

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.

Bibliographic Information

  • Book Title: Introduction to Deep Learning for Healthcare

  • Authors: Cao Xiao, Jimeng Sun

  • DOI: https://doi.org/10.1007/978-3-030-82184-5

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

  • Hardcover ISBN: 978-3-030-82183-8

  • eBook ISBN: 978-3-030-82184-5

  • Edition Number: 1

  • Number of Pages: XI, 232

  • Number of Illustrations: 1 b/w illustrations

  • Topics: Health Informatics, Machine Learning, Artificial Intelligence

Buying options

eBook USD 59.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-82184-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 74.99
Price excludes VAT (USA)