Machine Learning in Medicine by Ton J. CleophasMachine Learning in Medicine by Ton J. Cleophas

Machine Learning in Medicine

byTon J. Cleophas, Aeilko H. Zwinderman

Paperback | February 8, 2015

Pricing and Purchase Info

$124.50

Earn 623 plum® points

Prices and offers may vary in store

Quantity:

In stock online

Ships free on orders over $25

Not available in stores

about

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.
Title:Machine Learning in MedicineFormat:PaperbackDimensions:265 pagesPublished:February 8, 2015Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:9400793634

ISBN - 13:9789400793637

Look for similar items by category:

Reviews

Table of Contents

Preface.- 1 Introduction to machine learning.- 2 Logistic regression for health profiling.- 3 Optimal scaling: discretization.- 4 Optimal scaling: regularization including ridge, lasso, and elastic net regression.- 5 Partial correlations.- 6 Mixed linear modelling.- 7 Binary partitioning.- 8 Item response modelling.- 9 Time-dependent predictor modelling.- 10 Seasonality assessments.- 11 Non-linear modelling.- 12 Artificial intelligence, multilayer Perceptron modelling.- 13 Artificial intelligence, radial basis function modelling.- 14 Factor analysis.- 15 Hierarchical cluster analysis for unsupervised data.- 16 Partial least squares.- 17 Discriminant analysis for Supervised data.- 18 Canonical regression.- 19 Fuzzy modelling.- 20 Conclusions. Index.