LEADER 07662nam 22007455 450 001 9910299961303321 005 20200701134316.0 010 $a3-319-07413-X 024 7 $a10.1007/978-3-319-07413-9 035 $a(CKB)3710000000119157 035 $a(SSID)ssj0001244409 035 $a(PQKBManifestationID)11852030 035 $a(PQKBTitleCode)TC0001244409 035 $a(PQKBWorkID)11313814 035 $a(PQKB)11788041 035 $a(MiAaPQ)EBC1731165 035 $a(DE-He213)978-3-319-07413-9 035 $a(PPN)178780898 035 $a(EXLCZ)993710000000119157 100 $a20140527d2014 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning in Medicine - Cookbook Two /$fby Ton J. Cleophas, Aeilko H. Zwinderman 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (137 pages) $cillustrations, tables 225 1 $aSpringerBriefs in Statistics,$x2191-544X 300 $aIncludes index. 311 $a3-319-07412-1 327 $aPreface. I Cluster models -- Nearest Neighbors for Classifying New Medicines -- Predicting High-Risk-Bin Memberships -- Predicting Outlier Memberships -- Linear Models -- Polynomial Regression for Outcome Categories -- Automatic Nonparametric Tests for Predictor Categories- Random Intercept Models for Both Outcome and Predictor -- Automatic Regression for Maximizing Linear Relationships -- Simulation Models for Varying Predictors -- Generalized Linear Mixed Models for Outcome Prediction from Mixed Data -- Two Stage Least Squares for Linear Models with Problematic -- Autoregressive Models for Longitudinal Data. II Rules Models -- Item Response Modeling for Analyzing Quality of Life with Better Precision -- Survival Studies with Varying Risks of Dying -- Fuzzy Logic for Improved Precision of Pharmacological Data Analysis -- Automatic Data Mining for the Best Treatment of a Disease -- Pareto Charts for Identifying the Main Factors of Multifactorial -- Radial Basis Neural Networks for Multidimensional Gaussian -- Automatic Modeling for Drug Efficacy Prediction -- Automatic Modeling for Clinical Event Prediction -- Automatic Newton Modeling in Clinical Pharmacology -- Index. 330 $aThe amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible. Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the main incentive for the authors to complete a series of three textbooks entitled ?Machine Learning in Medicine Part One, Two and Three, Springer Heidelberg Germany, 2012-2013", describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details. For this reason we produced a 100 page cookbook, entitled "Machine Learning in Medicine - Cookbook One", with data examples available at extras.springer.com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled "Machine Learning in Medicine - Cookbook Two" is complementary to the first and also intended for providing a more balanced view of the field and thus, as a must-read not only for physicians and students, but also for any one involved in the process and progress of health and health care. Similarly to Machine Learning in Medicine - Cookbook One, the current work will describe stepwise analyses of over twenty machine learning methods, that are, likewise, based on the three major machine learning methodologies: Cluster methodologies (Chaps. 1-3) Linear methodologies (Chaps. 4-11) Rules methodologies (Chaps. 12-20) In extras.springer.com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the above three textbooks, an introduction is given to SPSS Modeler (SPSS' data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8. We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing "general purposes", "main scientific questions" and "conclusions" are given in place. Finally, we will demonstrate that modern machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method. 410 0$aSpringerBriefs in Statistics,$x2191-544X 606 $aMedicine 606 $aBiostatistics 606 $aStatistics  606 $aApplication software 606 $aBiometrics (Biology) 606 $aMedicine/Public Health, general$3https://scigraph.springernature.com/ontologies/product-market-codes/H00007 606 $aBiostatistics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15020 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aComputer Applications$3https://scigraph.springernature.com/ontologies/product-market-codes/I23001 606 $aBiometrics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22040 615 0$aMedicine. 615 0$aBiostatistics. 615 0$aStatistics . 615 0$aApplication software. 615 0$aBiometrics (Biology). 615 14$aMedicine/Public Health, general. 615 24$aBiostatistics. 615 24$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aComputer Applications. 615 24$aBiometrics. 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 $a9910299961303321 996 $aMachine Learning in Medicine - Cookbook Two$92532456 997 $aUNINA