TY  - JOUR
PY  - 2000//
TI  - Discovery of predictive models in an injury surveillance database: an application of data mining in clinical research
JO  - Proceedings of the AMIA symposium
A1  - Holmes, John H.
A1  - Durbin, D. R.
A1  - Winston, Flaura Koplin
SP  - 359
EP  - 363
VL  - 2000
IS  - 
N2  - 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>
LA  - en
SN  - 1531-605X
UR  - http://dx.doi.org/
ID  - ref1
ER  -