SpringerBriefs in Statistics

Machine Learning in Medicine - Cookbook

Authors: Cleophas, Ton J., Zwinderman, Aeilko H.

  • Machine learning is an innovation in the medical field
  • So far a book on the subject to a medical audience has not been published
  • The book is time-friendly
  • The book is multipurpose, (1) an introduction for the ignorant, (2) a primer to the inexperienced, (3) a self-assessment handbook for the advanced inexperienced, (4) a self-assessment handbook for the advanced
  • The methods selected and described have been tested in real life and by the authors
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Buy this book

eBook $39.99
price for USA (gross)
  • ISBN 978-3-319-04181-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $54.99
price for USA
  • ISBN 978-3-319-04180-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
About this book

The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data analysis have difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing.

Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning and the current 100 page cookbook should be helpful to that aim. It covers in a condensed form the subjects reviewed in the 750 page three volume textbook by the same authors, entitled “Machine Learning in Medicine I-III” (ed. by Springer, Heidelberg, Germany, 2013) and was written as a hand-hold presentation and must-read publication. It was written not only to investigators and students in the fields, but also to jaded clinicians new to the methods and lacking time to read the entire textbooks.

General purposes and scientific questions of the methods are only briefly mentioned, but full attention is given to the technical details. The two authors, a statistician and current president of the International Association of Biostatistics and a clinician and past-president of the American College of Angiology, provide plenty of step-by-step analyses from their own research and data files for self-assessment are available at extras.springer.com.

From their experience the authors demonstrate that machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.

Reviews

From the reviews:

“This is a concise, instructive and practical text on the various models of machine learning with particular reference to their applicability in medicine. … The book is primarily aimed at students, health professionals and researchers with basic experience in statistics who are looking for a quick review prior to using machine learning tools. … This book is a valuable resource for those who need a quick reference for machine learning models in medicine.” (Kamesh Sivagnanam, Doody’s Book Reviews, April, 2014)


Table of contents (20 chapters)

  • Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys (50 Patients)

    Cleophas, Ton J. (et al.)

    Pages 3-8

  • Density-Based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data (50 Patients)

    Cleophas, Ton J. (et al.)

    Pages 9-11

  • Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships in Individual Future Patients (120 Patients)

    Cleophas, Ton J. (et al.)

    Pages 13-15

  • Linear, Logistic, and Cox Regression for Outcome Prediction with Unpaired Data (20, 55, and 60 Patients)

    Cleophas, Ton J. (et al.)

    Pages 19-27

  • Generalized Linear Models for Outcome Prediction with Paired Data (100 Patients and 139 Physicians)

    Cleophas, Ton J. (et al.)

    Pages 29-35

Buy this book

eBook $39.99
price for USA (gross)
  • ISBN 978-3-319-04181-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $54.99
price for USA
  • ISBN 978-3-319-04180-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning in Medicine - Cookbook
Authors
Series Title
SpringerBriefs in Statistics
Copyright
2014
Publisher
Springer International Publishing
Copyright Holder
The Author(s)
eBook ISBN
978-3-319-04181-0
DOI
10.1007/978-3-319-04181-0
Softcover ISBN
978-3-319-04180-3
Series ISSN
2191-544X
Edition Number
1
Number of Pages
XI, 137
Number of Illustrations and Tables
14 b/w illustrations
Topics