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© 2014

Machine Learning in Medicine - Cookbook

Benefits

  • 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

Book

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Cluster Models

  3. Linear Models

  4. Rules Models

    1. Front Matter
      Pages 79-79
    2. Ton J. Cleophas, Aeilko H. Zwinderman
      Pages 85-90
    3. Ton J. Cleophas, Aeilko H. Zwinderman
      Pages 97-104

About this book

Introduction

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.

Keywords

Computer science Data mining Machine learning SPSS Modeler SPSS statistical software

Authors and affiliations

  1. 1.Department Medicine, Albert Schweitzer HospitalSliedrechtThe Netherlands
  2. 2.Academic Medical CenterDepartment Biostatistics and Epidemiology, Academic Medical CenterAmsterdamThe Netherlands

Bibliographic information

Industry Sectors
Pharma
Health & Hospitals
Biotechnology
Law
Consumer Packaged Goods

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)