LEADER 05391nam 22008295 450 001 9910437844503321 005 20200630151319.0 010 $a94-007-5824-3 024 7 $a10.1007/978-94-007-5824-7 035 $a(CKB)2670000000338258 035 $a(EBL)1083657 035 $a(OCoLC)828302727 035 $a(SSID)ssj0000879230 035 $a(PQKBManifestationID)11442825 035 $a(PQKBTitleCode)TC0000879230 035 $a(PQKBWorkID)10850540 035 $a(PQKB)11666116 035 $a(DE-He213)978-94-007-5824-7 035 $a(MiAaPQ)EBC6314789 035 $a(MiAaPQ)EBC1083657 035 $a(Au-PeEL)EBL1083657 035 $a(CaPaEBR)ebr10969030 035 $a(PPN)168341867 035 $a(EXLCZ)992670000000338258 100 $a20130217d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning in Medicine$b[electronic resource] /$fby Ton J. Cleophas, Aeilko H. Zwinderman 205 $a1st ed. 2013. 210 1$aDordrecht :$cSpringer Netherlands :$cImprint: Springer,$d2013. 215 $a1 online resource (270 p.) 300 $aDescription based upon print version of record. 311 $a94-007-9363-4 311 $a94-007-5823-5 320 $aIncludes bibliographical references and index. 327 $aPreface -- 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.                                                                                                                                                                                                                                                                                                                                                . 330 $aMachine 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. 606 $aMedicine 606 $aEntomology 606 $aStatistics  606 $aOptical data processing 606 $aLiteracy 606 $aBiomedicine, general$3https://scigraph.springernature.com/ontologies/product-market-codes/B0000X 606 $aEntomology$3https://scigraph.springernature.com/ontologies/product-market-codes/L25090 606 $aMedicine/Public Health, general$3https://scigraph.springernature.com/ontologies/product-market-codes/H00007 606 $aStatistics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/S0000X 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aLiteracy$3https://scigraph.springernature.com/ontologies/product-market-codes/O40000 615 0$aMedicine. 615 0$aEntomology. 615 0$aStatistics . 615 0$aOptical data processing. 615 0$aLiteracy. 615 14$aBiomedicine, general. 615 24$aEntomology. 615 24$aMedicine/Public Health, general. 615 24$aStatistics, general. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aLiteracy. 676 $a610.28563 700 $aCleophas$b Ton J$4aut$4http://id.loc.gov/vocabulary/relators/aut$0472359 702 $aZwinderman$b Aeilko H$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437844503321 996 $aMachine Learning in Medicine$92507524 997 $aUNINA