title: Discovery of predictive models in an injury surveillance database: an application of data mining in clinical research.
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.
authors.affiliation: University of Pennsylvania Medical Center, Philadelphia, PA, USA.
authors.forename: J H
authors.initials: JH
authors.lastname: Holmes
authors.forename: D R
authors.initials: DR
authors.lastname: Durbin
authors.forename: F K
authors.initials: FK
authors.lastname: Winston
mesh_terms: D001185:Artificial Intelligence; D002965:Classification / Q000379:methods*; D006259:Craniocerebral Trauma / Q000145:classification / Q000453:epidemiology*; D016208:Databases, Factual; D003663:Decision Trees; D006801:Humans; D016015:Logistic Models; D011159:Population Surveillance; D012106:Research