Machine Learning for Personalized Medicine: Predicting Primary Myocardial Infarction from Electronic Health Records

  • Jeremy C. Weiss University of Wisconsin-Madison
  • Sriraam Natarajan Wake Forest University
  • Peggy L. Peissig Marshfield Clinic Research Foundation
  • Catherine A. McCarty Essentia Institute of Rural Health
  • David Page University of Wisconsin-Madison

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.
Published
2012-12-21
How to Cite
Weiss, J. C., Natarajan, S., Peissig, P. L., McCarty, C. A., & Page, D. (2012). Machine Learning for Personalized Medicine: Predicting Primary Myocardial Infarction from Electronic Health Records. AI Magazine, 33(4), 33. https://doi.org/10.1609/aimag.v33i4.2438
Section
Articles