title: Patient-centered yes/no prognosis using learning machines.
abstract: In the last 15 years several machine learning approaches have been developed for classification and regression. In an intuitive manner we introduce the main ideas of classification and regression trees, support vector machines, bagging, boosting and random forests. We discuss differences in the use of machine learning in the biomedical community and the computer sciences. We propose methods for comparing machines on a sound statistical basis. Data from the German Stroke Study Collaboration is used for illustration. We compare the results from learning machines to those obtained by a published logistic regression and discuss similarities and differences.
authors.affiliation: Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany.
authors.forename: I R
authors.initials: IR
authors.lastname: König
authors.forename: J D
authors.initials: JD
authors.lastname: Malley
authors.forename: S
authors.initials: S
authors.lastname: Pajevic
authors.forename: C
authors.initials: C
authors.lastname: Weimar
authors.forename: H-C
authors.initials: HC
authors.lastname: Diener
authors.forename: A
authors.initials: A
authors.lastname: Ziegler
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002138:Calibration; D019295:Computational Biology / Q000379:methods*; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D015233:Models, Statistical; D011379:Prognosis; D012044:Regression Analysis; D015203:Reproducibility of Results; D020521:Stroke / Q000175:diagnosis / Q000628:therapy