title: A tree-based decision model to support prediction of the severity of asthma exacerbations in children.
abstract: This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool.
authors: Department of Pediatrics and Emergency Medicine, University of Ottawa, Ottawa, ON, Canada.
authors: Ken
authors: K
authors: Farion
authors: Wojtek
authors: W
authors: Michalowski
authors: Szymon
authors: S
authors: Wilk
authors: Dympna
authors: D
authors: O'Sullivan
authors: Stan
authors: S
authors: Matwin
mesh_terms: D000293:Adolescent; D001249:Asthma / Q000175:diagnosis*; D002648:Child; D002675:Child, Preschool; D003658:Decision Making, Computer-Assisted; D003663:Decision Trees; D018450:Disease Progression; D004636:Emergency Service, Hospital; D006776:Hospitals, Pediatric; D006801:Humans; D007223:Infant; D011379:Prognosis; D012189:Retrospective Studies