Open AccessMachine Learning for Personalized Medicine: Predicting Primary Myocardial Infarction from Electronic Health Records
Author(s) -
Weiss Jeremy C.,
Natarajan Sriraam,
Peissig Peggy L.,
McCarty Catherine A.,
Page David
Publication year - 2012
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v33i4.2438
Subject(s) - gradient boosting , computer science , boosting (machine learning) , recall , task (project management) , machine learning , statistical relational learning , health records , myocardial infarction , artificial intelligence , relational database , data mining , medicine , psychology , cognitive psychology , health care , engineering , systems engineering , random forest , economics , economic growth
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.