SpringerBriefs in Statistics

Machine Learning in Medicine - Cookbook Two

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
  • The authors are from both a mathematical and medical background, which is adequate, because machine learning is a discipline at the interface of bioscience and mathematics
  • The methods selected and described have been tested in real life and by the authors
see more benefits

Buy this book

eBook $39.99
price for USA (gross)
  • ISBN 978-3-319-07413-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $54.99
price for USA
  • ISBN 978-3-319-07412-2
  • 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 medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has 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. Consequently, proper data-based health decisions will soon be impossible.

Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the main incentive for the authors to complete a series of three textbooks entitled “Machine Learning in Medicine Part One, Two and Three, Springer Heidelberg Germany, 2012-2013", describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details.

For this reason we produced a 100 page cookbook, entitled "Machine Learning in Medicine - Cookbook One", with data examples available at extras.springer.com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled "Machine Learning in Medicine - Cookbook Two" is complementary to the first and also intended for providing a more balanced view of the field and thus, as a must-read not only for physicians and students, but also for any one involved in the process and progress of health and health care.

Similarly to Machine Learning in Medicine - Cookbook One, the current work will describe stepwise analyses of over twenty machine learning methods, that are, likewise, based on the three major machine learning methodologies:

  • Cluster methodologies (Chaps. 1-3)
  • Linear methodologies (Chaps. 4-11)
  • Rules methodologies (Chaps. 12-20)

In extras.springer.com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the above three textbooks, an introduction is given to SPSS Modeler (SPSS' data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8.

We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing "general purposes", "main scientific questions" and "conclusions" are given in place.

Finally, we will demonstrate that modern 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 book reviews:

“This book serves as a complement to the first Machine Learning in Medicine cookbook. … It is aimed primarily 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 is a valuable resource for those who need a quick reference for machine learning models in medicine.” (Kamesh Sivagnanam, Doody’s Book Reviews, September, 2014)

Table of contents (20 chapters)

  • Nearest Neighbors for Classifying New Medicines (2 New and 25 Old Opioids)

    Cleophas, Ton J. (et al.)

    Pages 3-10

  • Predicting High-Risk-Bin Memberships (1,445 Families)

    Cleophas, Ton J. (et al.)

    Pages 11-15

  • Predicting Outlier Memberships (2,000 Patients)

    Cleophas, Ton J. (et al.)

    Pages 17-20

  • Polynomial Regression for Outcome Categories (55 Patients)

    Cleophas, Ton J. (et al.)

    Pages 23-26

  • Automatic Nonparametric Tests for Predictor Categories (60 and 30 Patients)

    Cleophas, Ton J. (et al.)

    Pages 27-35

Buy this book

eBook $39.99
price for USA (gross)
  • ISBN 978-3-319-07413-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $54.99
price for USA
  • ISBN 978-3-319-07412-2
  • 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
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Machine Learning in Medicine - Cookbook Two
Authors
Series Title
SpringerBriefs in Statistics
Copyright
2014
Publisher
Springer International Publishing
Copyright Holder
The Author(s)
eBook ISBN
978-3-319-07413-9
DOI
10.1007/978-3-319-07413-9
Softcover ISBN
978-3-319-07412-2
Series ISSN
2191-544X
Edition Number
1
Number of Pages
XI, 140
Number of Illustrations and Tables
49 b/w illustrations
Topics