Machine learning in medicine

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Bibliographic Details
Authors / Creators: Cleophas, Ton J. M. (Author), Zwinderman, Aeilko H. (Author)
Other Authors / Creators:Zwinderman, Aeilko H., author.
Format: Electronic eBook
Language:English
Imprint: Dordrecht ; New York : Springer, [2013]
Subjects:
Online Access:Available in Springer Biomedical and Life Sciences eBooks 2013 English/International.
Description
Summary: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.
Bibliography:Includes bibliographical references and index.
ISBN:9789400758247 (online)