Overview
- 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
- Includes supplementary material: sn.pub/extras
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Table of contents (20 chapters)
Reviews
From the reviews:
“This novel book on machine learning in medicine deals with statistical methods for analyzing complex data involving multiple variables. … The intended audience includes physicians, clinical researchers, physicians in training, statisticians, and medical students, as well as master’s and doctoral students in epidemiology and biostatistics. … The language is simple and the chapters are well organized. This will be an excellent resource for a quick review of machine learning in medicine, particularly in genetic research, clinical trials, and adverse drug surveillance.” (Parthiv Amin, Doody’s Book Reviews, September, 2013)Authors and Affiliations
Bibliographic Information
Book Title: Machine Learning in Medicine
Authors: Ton J. Cleophas, Aeilko H. Zwinderman
DOI: https://doi.org/10.1007/978-94-007-5824-7
Publisher: Springer Dordrecht
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: Springer Science+Business Media Dordrecht 2013
Hardcover ISBN: 978-94-007-5823-0Published: 27 February 2013
Softcover ISBN: 978-94-007-9363-7Published: 08 February 2015
eBook ISBN: 978-94-007-5824-7Published: 12 February 2013
Edition Number: 1
Number of Pages: XV, 265
Number of Illustrations: 44 b/w illustrations
Topics: Biomedicine general, Entomology, Medicine/Public Health, general, Statistics, general, Computer Imaging, Vision, Pattern Recognition and Graphics, Literacy