A tree-based decision model to support prediction of the severity of asthma exacerbations in children


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.

Publication DOI: https://doi.org/10.1007/s10916-009-9268-7
Divisions: ?? 50811700Jl ??
Additional Information: The original publication is available at www.springerlink.com
Uncontrolled Keywords: decision making,asthma,child,retrospective studies,decision trees,Information Systems,Medicine (miscellaneous),Health Informatics,Health Information Management
Publication ISSN: 1573-689X
Last Modified: 24 Apr 2024 07:09
Date Deposited: 16 Dec 2011 12:53
Full Text Link: http://www.spri ... 40v4721132402p/
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2010-08
Authors: Farion, Ken
Michalowski, Wojtek
Wilk, Szymon
O'Sullivan, Dympna M.
Matwin, Stan


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