@article{ref1,
title="Discovery of predictive models in an injury surveillance database: an application of data mining in clinical research",
journal="Proceedings of the AMIA symposium",
year="2000",
author="Holmes, John H. and Durbin, D. R. and Winston, Flaura Koplin",
volume="2000",
number="",
pages="359-363",
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.<p /> <p>Language: en</p>",
language="en",
issn="1531-605X",
doi="",
url="http://dx.doi.org/"
}