An edition of Machine Learning in Medicine (2013)

Machine Learning in Medicine

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Last edited by MARC Bot
July 5, 2019 | History
An edition of Machine Learning in Medicine (2013)

Machine Learning in Medicine

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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.

Publish Date
Language
English
Pages
265

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Previews available in: English

Edition Availability
Cover of: Machine Learning in Medicine
Machine Learning in Medicine
2013, Springer Netherlands, Imprint: Springer
electronic resource / in English

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Book Details


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. .

Edition Notes

Published in
Dordrecht

Classifications

Dewey Decimal Class
610
Library of Congress
R-RZ

The Physical Object

Format
[electronic resource] /
Pagination
XV, 265 p. 44 illus.
Number of pages
265

ID Numbers

Open Library
OL27072045M
Internet Archive
machinelearningm00cleo_049
ISBN 13
9789400758247

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