Overview
First publication of a complete overview of machine learning methodologies for the medical and health sector
Written as a training companion, and as a must-read, not only for physicians and students, but also for anyone involved in the process and progress of health and health care
In 80 chapters 80 different machine learning methodologies are reviewed, in combination with a data example for self-assessment
Each chapter can be studied without the need to consult other chapters
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Table of contents (80 chapters)
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Front Matter
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Cluster and Classification Models
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Front Matter
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Keywords
About this book
The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector. It was written as a training companion and 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. In eighty chapters eighty different machine learning methodologies are reviewed, in combination with data examples for self-assessment. Each chapter can be studied without the need to consult other chapters.
The amount of data stored in the world's databases doubles every 20 months, and clinicians, familiar with traditional statistical methods, are at a loss to analyze them. Traditional methods have, indeed, difficulty to identify outliers in large datasets, and to find patterns in big data and data with multiple exposure / outcome variables. In addition, analysis-rules for surveys and questionnaires, which are currently common methods of data collection, are, essentially, missing. Fortunately, the new discipline, machine learning, is able to cover all of these limitations.
So far medical professionals have been rather reluctant to use machine learning. Also, in the field of diagnosis making, few doctors may want a computer checking them, are interested in collaboration with a computer or with computer engineers. Adequate health and health care will, however, soon be impossible without proper data supervision from modern machine learning methodologies like cluster models, neural networks and other data mining methodologies.
Each chapter starts with purposes and scientific questions. Then, step-by-step analyses, using data examples, are given. Finally, a paragraph with conclusion, and references to the corresponding sites of three introductory textbooks, previously written by the same authors, is given.
Reviews
“This is designed to be the first complete overview of machine learning methodologies for the medical and health sector. … It is targeted primarily at medical and healthcare sector personnel, physicians and students, as well as anyone involved in the process of health and healthcare. … This is an excellent resource for medical professionals wishing to understand and use machine learning methods.” (Kamesh Sivagnanam, Doody's Book Reviews, June, 2015)
Authors and Affiliations
Bibliographic Information
Book Title: Machine Learning in Medicine - a Complete Overview
Authors: Ton J. Cleophas, Aeilko H. Zwinderman
DOI: https://doi.org/10.1007/978-3-319-15195-3
Publisher: Springer Cham
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Softcover ISBN: 978-3-319-38638-6Published: 09 October 2016
eBook ISBN: 978-3-319-15195-3Published: 27 March 2015
Edition Number: 1
Number of Pages: XXIV, 516
Number of Illustrations: 159 b/w illustrations
Topics: Biomedicine general, Medicine/Public Health, general, Statistics, general, Science, Humanities and Social Sciences, multidisciplinary