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Machine Learning in Medicine

  • Textbook
  • © 2013

Overview

Authors:
  1. Ton J. Cleophas

    1. Sliedrecht, Netherlands

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    You can also search for this author in PubMed   Google Scholar

  2. Aeilko H. Zwinderman

    1. , Dept. Epidemiology and Biostatistics, Academic Medical Center, Amsterdam, Netherlands

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Electronic health records of modern health facilities, are increasingly complex and systematic assessment of these records is virtually impossible without special computationally intensive methods
  • Clinicians and other health professionals are not familiar with these methods, and this book is the first publication that systematically reviews such methods, particularly, for this audience
  • The book is written as a hand-hold presentation also accessible to non-mathematicians, and as a must-read publication for those new to the methods
  • The book includes step by step data analyses in SPSS, and can, therefore, also be used as a cookbook-like guide for those starting with the novel methodologies
  • Includes supplementary material: sn.pub/extras

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Table of contents (20 chapters)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Introduction to Machine Learning

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 1-15

  3. Logistic Regression for Health Profiling

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 17-24

  4. Optimal Scaling: Discretization

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 25-38

  5. Optimal Scaling: Regularization Including Ridge, Lasso, and Elastic Net Regression

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 39-53

  6. Partial Correlations

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 55-64

  7. Mixed Linear Models

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 65-77

  8. Binary Partitioning

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 79-86

  9. Item Response Modeling

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 87-98

  10. Time-Dependent Predictor Modeling

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 99-111

  11. Seasonality Assessments

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 113-126

  12. Non-linear Modeling

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 127-143

  13. Artificial Intelligence, Multilayer Perceptron Modeling

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 145-156

  14. Artificial Intelligence, Radial Basis Functions

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 157-166

  15. Factor Analysis

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 167-181

  16. Hierarchical Cluster Analysis for Unsupervised Data

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 183-195

  17. Partial Least Squares

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 197-213

  18. Discriminant Analysis for Supervised Data

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 215-224

  19. Canonical Regression

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 225-240

  20. Fuzzy Modeling

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 241-253
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Keywords

About this book

Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods.

Reviews

From the reviews:

“This novel book on machine learning in medicine deals with statistical methods for analyzing complex data involving multiple variables. … The intended audience includes physicians, clinical researchers, physicians in training, statisticians, and medical students, as well as master’s and doctoral students in epidemiology and biostatistics. … The language is simple and the chapters are well organized. This will be an excellent resource for a quick review of machine learning in medicine, particularly in genetic research, clinical trials, and adverse drug surveillance.” (Parthiv Amin, Doody’s Book Reviews, September, 2013)

Authors and Affiliations

  • Sliedrecht, Netherlands

    Ton J. Cleophas

  • , Dept. Epidemiology and Biostatistics, Academic Medical Center, Amsterdam, Netherlands

    Aeilko H. Zwinderman

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