|Title||Discovery of predictive models in an injury surveillance database: an application of data mining in clinical research.|
|Publication Type||Journal Article|
|Year of Publication||2000|
|Authors||Holmes JH, Durbin D, Winston FK|
|Journal||Proc AMIA Symp|
|Keywords||Artificial Intelligence, Classification, Craniocerebral Trauma, Databases, Factual, Decision Trees, Humans, Logistic Models, Population Surveillance, Research|
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.
|Alternate Journal||Proc AMIA Symp|