Jeremy C. Weiss, Sriraam Natarajan, P. Peissig, C. McCarty, David Page
{"title":"Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records","authors":"Jeremy C. Weiss, Sriraam Natarajan, P. Peissig, C. McCarty, David Page","doi":"10.1609/aaai.v26i2.18981","DOIUrl":null,"url":null,"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.","PeriodicalId":74524,"journal":{"name":"Proceedings of the ... Innovative Applications of Artificial Intelligence Conference. Innovative Applications of Artificial Intelligence Conference","volume":"2012 1","pages":"2341-2347"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... Innovative Applications of Artificial Intelligence Conference. Innovative Applications of Artificial Intelligence Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1609/aaai.v26i2.18981","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
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.