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Proc AMIA Symp. 2000: 359–363.
PMCID: PMC2243855
Discovery of predictive models in an injury surveillance database: an application of data mining in clinical research.
J. H. Holmes, D. R. Durbin, and F. K. Winston
University of Pennsylvania Medical Center, Philadelphia, PA, USA.
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.
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