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Machine Learning in Medicine

Part Three

  • Textbook
  • © 2013

Overview

Authors:
  1. Ton J. Cleophas

    1. Department Medicine, Albert Schweitzer Hospital, Sliedrecht, The Netherlands

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    You can also search for this author in PubMed   Google Scholar

  2. Aeilko H. Zwinderman

    1. Department Biostatistics and Epidemiology, Academic Medical Center, Amsterdam, The 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 machine learning

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Table of contents (20 chapters)

  1. Front Matter

    Pages i-xix
    Download chapter PDF

  2. Introduction to Machine Learning in Medicine Part Three

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 1-9

  3. Evolutionary Operations

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 11-18

  4. Multiple Treatments

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 19-28

  5. Multiple Endpoints

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 29-36

  6. Optimal Binning

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 37-46

  7. Exact P-Values and Their Interpretation

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 47-61

  8. Probit Regression

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 63-68

  9. Over-Dispersion

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 69-79

  10. Random Effects

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 81-94

  11. Weighted Least Squares

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 95-103

  12. Multiple Response Sets

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 105-113

  13. Complex Samples

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 115-125

  14. Runs Test

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 127-135

  15. Decision Trees

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 137-150

  16. Spectral Plots

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 151-160

  17. Newton’s Methods

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 161-172

  18. Stochastic Processes: Stationary Markov Chains

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 173-181

  19. Stochastic Processes: Absorbing Markov Chains

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 183-193

  20. Conjoint Analysis

    • Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 195-203
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About this book

Machine learning is concerned with the analysis of large data and multiple variables. It is also often more sensitive than traditional statistical methods to analyze small data. The first and second volumes reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, fuzzy modeling, various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, association rule learning, anomaly detection, and correspondence analysis. This third volume addresses more advanced methods and includes subjects like evolutionary programming, stochastic methods, complex sampling, optional binning, Newton's methods, decision trees, and other subjects. Both the theoretical bases and the step by step analyses are described for the benefit of non-mathematical readers. Each chapter can be studied without the need to consult other chapters. Traditional statistical tests are, sometimes, priors to machine learning methods, and they are also, sometimes, used as contrast tests. To those wishing to obtain more knowledge of them, we recommend to additionally study (1) Statistics Applied to Clinical Studies 5th Edition 2012, (2) SPSS for Starters Part One and Two 2012, and (3) Statistical Analysis of Clinical Data on a Pocket Calculator Part One and Two 2012, written by the same authors, and edited by Springer, New York.

Reviews

From the reviews:

“This book is excellent. It is valuable source of a basic understanding of novel machine learning methods of clinical data analysis and can be used as a reference by students and teachers of epidemiology, statistics and biostatistics, computer and social scientists, and clinical investigators.” (Vedang J. Bhavsar, Doody’s Book Reviews, March, 2014)

Authors and Affiliations

  • Department Medicine, Albert Schweitzer Hospital, Sliedrecht, The Netherlands

    Ton J. Cleophas

  • Department Biostatistics and Epidemiology, Academic Medical Center, Amsterdam, The Netherlands

    Aeilko H. Zwinderman

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