Machine Learning in Medicine

Buch | Hardcover
265 Seiten
2013
Springer (Verlag)
978-94-007-5823-0 (ISBN)

Lese- und Medienproben

Machine Learning in Medicine - Ton J. Cleophas, Aeilko H. Zwinderman
96,29 inkl. MwSt
Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. 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.
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.

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.

                                                                                   

                                                                                   



                                                                                   



                                                                                   

Erscheint lt. Verlag 27.2.2013
Zusatzinfo 44 Illustrations, black and white; XV, 265 p. 44 illus.
Verlagsort Dordrecht
Sprache englisch
Maße 155 x 235 mm
Themenwelt Geisteswissenschaften Sprach- / Literaturwissenschaft Sprachwissenschaft
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Mathematik / Informatik Mathematik
Medizin / Pharmazie Allgemeines / Lexika
Medizin / Pharmazie Studium
Naturwissenschaften Biologie Zoologie
Schlagworte cluster analysis • Data Mining • discriminant analysis • Factor Analysis • machine learning
ISBN-10 94-007-5823-5 / 9400758235
ISBN-13 978-94-007-5823-0 / 9789400758230
Zustand Neuware
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