Citation

Holmes JH, Durbin DR, Winston FK. Proc. AMIA Symp. 2000; 2000: 359-363.

Affiliation

University of Pennsylvania Medical Center, Philadelphia, PA, USA.

Copyright

(Copyright © 2000, Hanley and Belfus)

DOI

unavailable

PMID

11079905

PMCID

PMC2243855

Abstract

A new, evolutionary computation-based approach to discovering prediction models in surveillance data was developed and evaluated. This approach was operationalized in EpiCS, a type of learning classifier system specially adapted to model clinical data. In applying EpiCS to a large, prospective injury surveillance database, EpiCS was found to create accurate predictive models quickly that were highly robust, being able to classify > 99% of cases early during training. After training, EpiCS classified novel data more accurately (p < 0.001) than either logistic regression or decision tree induction (C4.5), two traditional methods for discovering or building predictive models.

Language: en