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 is the second volume of a novel publication on machine learning in medicine that details statistical analysis of complex data with many variables. … The intended audience includes physicians, clinical researchers, physicians in training, statisticians, and medical students as well as master’s and doctoral students in biostatistics and epidemiology. … The simple language and well-organized chapters are unsurpassed attributes of this book. It is an exceptional resource for a quick review of machine learning in medicine.” (Goral Panchal, Doody’s Book Reviews, October, 2013)Authors and Affiliations
Bibliographic Information
Book Title: Machine Learning in Medicine
Book Subtitle: Part Two
Authors: Ton J. Cleophas, Aeilko H. Zwinderman
DOI: https://doi.org/10.1007/978-94-007-6886-4
Publisher: Springer Dordrecht
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: Springer Science+Business Media Dordrecht 2013
Hardcover ISBN: 978-94-007-6885-7Published: 12 June 2013
Softcover ISBN: 978-94-007-9512-9Published: 13 June 2015
eBook ISBN: 978-94-007-6886-4Published: 30 May 2013
Edition Number: 1
Number of Pages: XIV, 231
Topics: Biomedicine general, Entomology, Medicine/Public Health, general, Statistics, general, Computer Imaging, Vision, Pattern Recognition and Graphics, Literacy