title: Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records.
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.
authors.affiliation: University of Wisconsin-Madison 1300 University Ave, Madison,WI {jcweiss,page}@biostat.wisc.edu.
authors.forename: Jeremy C
authors.initials: JC
authors.lastname: Weiss
authors.affiliation: University of Wisconsin-Madison 1300 University Ave, Madison,WI {jcweiss,page}@biostat.wisc.edu.
authors.forename: David
authors.initials: D
authors.lastname: Page
authors.affiliation: Marshfield Clinic Research Foundation 1000 North Oak Ave, Marshfield,WI peissig@mcrf.mfldclin.edu.
authors.forename: Peggy L
authors.initials: PL
authors.lastname: Peissig
authors.affiliation: Wake Forest University Baptist Medical Center Medical Center Blvd, Winston-Salem, NC snataraj@wakehealth.edu.
authors.forename: Sriraam
authors.initials: S
authors.lastname: Natarajan
authors.affiliation: Essentia Institute of Rural Health 502 E 2nd St, Duluth, MN cmccarty@eirh.org.
authors.forename: Catherine
authors.initials: C
authors.lastname: McCarty