Display options
Cite
Holmes JH, Durbin DR, Winston FK. Discovery of predictive models in an injury surveillance database: an application of data mining in clinical research. Proc AMIA Symp. 2000;359-63
Holmes, J. H., Durbin, D. R., & Winston, F. K. (2000). Discovery of predictive models in an injury surveillance database: an application of data mining in clinical research. Proceedings. AMIA Symposium, 359-63.
Holmes, J H, et al. "Discovery of predictive models in an injury surveillance database: an application of data mining in clinical research." Proceedings. AMIA Symposium vol. (2000): 359-63.
Holmes JH, Durbin DR, Winston FK. Discovery of predictive models in an injury surveillance database: an application of data mining in clinical research. Proc AMIA Symp. 2000;359-63. PMID: 11079905; PMCID: PMC2243855.
Copy Download .nbib Format: NLM
Share it on

Proc AMIA Symp. 2000;359-63.

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

Proceedings. AMIA Symposium

J H Holmes, D R Durbin, F K Winston

Affiliations

  1. University of Pennsylvania Medical Center, Philadelphia, PA, USA.

PMID: 11079905 PMCID: PMC2243855

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.

References

  1. Artif Intell Med. 2000 May;19(1):53-74 - PubMed

MeSH terms

Publication Types