Statistical relational learning to predict primary myocardial infarction from electronic health records

Jeremy C. Weiss, David Page, Sriraam Natarajan, Peggy L. Peissig, Catherine McCarty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Electronic health records (EHRs) are an emerging relational domain with large potential to improve clinical outcomes. We apply two statistical relational learning (SRL) algorithms to the task of predicting primary myocardial infarction. We show that one SRL algorithm, relational functional gradient boosting, outperforms propositional learners particularly in the medically-relevant high recall region. We observe that both SRL algorithms predict outcomes better than their propositional analogs and suggest how our methods can augment current epidemiological practices.

Original languageEnglish (US)
Title of host publicationAAAI-12 / IAAI-12 - Proceedings of the 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference
Pages2341-2347
Number of pages7
StatePublished - 2012
Event26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12 - Toronto, ON, Canada
Duration: Jul 22 2012Jul 26 2012

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume3

Other

Other26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12
CountryCanada
CityToronto, ON
Period7/22/127/26/12

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  • Cite this

    Weiss, J. C., Page, D., Natarajan, S., Peissig, P. L., & McCarty, C. (2012). Statistical relational learning to predict primary myocardial infarction from electronic health records. In AAAI-12 / IAAI-12 - Proceedings of the 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference (pp. 2341-2347). (Proceedings of the National Conference on Artificial Intelligence; Vol. 3).