Discovery of predictive models in an injury surveillance database: an application of data mining in clinical research.

TitleDiscovery of predictive models in an injury surveillance database: an application of data mining in clinical research.
Publication TypeJournal Article
Year of Publication2000
AuthorsHolmes JH, Durbin D, Winston FK
JournalProc AMIA Symp
Pagination359-63
Date Published2000
ISSN1531-605X
KeywordsArtificial Intelligence, Classification, Craniocerebral Trauma, Databases, Factual, Decision Trees, Humans, Logistic Models, Population Surveillance, 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.

Alternate JournalProc AMIA Symp
PubMed ID11079905