Machine Learning in Medicine

Nonfiction, Science & Nature, Science, Biological Sciences, Entomology, Mathematics, Statistics, Health & Well Being, Medical
Cover of the book Machine Learning in Medicine by Ton J. Cleophas, Aeilko H. Zwinderman, Springer Netherlands
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Author: Ton J. Cleophas, Aeilko H. Zwinderman ISBN: 9789400758247
Publisher: Springer Netherlands Publication: February 12, 2013
Imprint: Springer Language: English
Author: Ton J. Cleophas, Aeilko H. Zwinderman
ISBN: 9789400758247
Publisher: Springer Netherlands
Publication: February 12, 2013
Imprint: Springer
Language: English

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.

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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.

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