Machine Learning in Medicine

By: Ton J. Cleophas, Aeilko H. Zwinderman

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Hardcover | 27 February 2013

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Hardcover
284 Pages

Dimensions(cm)
23.5 x 15.88 x 1.91

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Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods.

Industry 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)

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