Machine Learning in Medicine
By: by Ton J. Cleophas, Aeilko H. Zwinderman
Contributors: Zwinderman, Aeilko H.; SpringerLink (Online service)
Publisher: Springer Netherlands; Imprint: Springer
Published: 2013
Place of publication: Dordrecht
Language: eng
Format: [electronic resource] /
Number of pages: 265
ISBN-13: 9789400758247
Subjects: Computer Imaging, Vision, Pattern Recognition and Graphics; Biomedicine; Statistics; Statistics, general; Biomedicine general; Entomology; Medicine/Public Health, general; Computer vision; Medicine; Literacy
Description: 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.
Table of contents: title: Preface; title: 1 Introduction to machine learning; title: 2 Logistic regression for health profiling; title: 3 Optimal scaling: discretization; title: 4 Optimal scaling: regularization including ridge, lasso, and elastic net regression; title: 5 Partial correlations; title: 6 Mixed linear modelling; title: 7 Binary partitioning; title: 8 Item response modelling; title: 9 Time-dependent predictor modelling; title: 10 Seasonality assessments; title: 11 Non-linear modelling; title: 12 Artificial intelligence, multilayer Perceptron modelling; title: 13 Artificial intelligence, radial basis function modelling; title: 14 Factor analysis; title: 15 Hierarchical cluster analysis for unsupervised data; title: 16 Partial least squares; title: 17 Discriminant analysis for Supervised data; title: 18 Canonical regression; title: 19 Fuzzy modelling; title: 20 Conclusions. Index.                                                                                                                                                                                                                                                                                                                                               .