title: Decision path models for patient-specific modeling of patient outcomes.
abstract: Patient-specific models are constructed to take advantage of the particular features of the patient case of interest compared to commonly used population-wide models that are constructed to perform well on average on all cases. We introduce two patient-specific algorithms that are based on the decision tree paradigm. These algorithms construct a decision path specific for each patient of interest compared to a single population-wide decision tree with many paths that is applicable to all patients of interest that are constructed by standard algorithms. We applied the patient-specific algorithms to predict five different outcomes in clinical datasets. Compared to the population-wide CART decision tree the patient-specific decision path models had superior performance on area under the ROC curve (AUC) and had comparable performance on balanced accuracy. Our results provide support for patient-specific algorithms being a promising approach for predicting clinical outcomes.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA.
authors.forename: Antonio
authors.initials: A
authors.lastname: Ferreira
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA.
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA.
authors.forename: Shyam
authors.initials: S
authors.lastname: Visweswaran
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D003661:Decision Support Techniques; D003663:Decision Trees; D006801:Humans; D005791:Patient Care; D011379:Prognosis; D016896:Treatment Outcome