Author | |
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.
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Year of Publication |
2000
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Journal |
Proc AMIA Symp
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Number of Pages |
359-63
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Date Published |
2000
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ISSN Number |
1531-605X
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Alternate Journal |
Proc AMIA Symp
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PMID |
11079905
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