Learning to predict post-hospitalization VTE risk from EHR data.

Emily Kawaler, Alexander Cobian, Peggy Peissig, Deanna Cross, Steve Yale, Mark Craven

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

We consider the task of predicting which patients are most at risk for post-hospitalization venothromboembolism (VTE) using information automatically elicited from an EHR. Given a set of cases and controls, we use machine-learning methods to induce models for making these predictions. Our empirical evaluation of this approach offers a number of interesting and important conclusions. We identify several risk factors for VTE that were not previously recognized. We show that machine-learning methods are able to induce models that identify high-risk patients with accuracy that exceeds previously developed scoring models for VTE. Additionally, we show that, even without having prior knowledge about relevant risk factors, we are able to learn accurate models for this task.

Original languageEnglish
Pages (from-to)436-445
Number of pages10
JournalUnknown Journal
Volume2012
StatePublished - 2012

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    Kawaler, E., Cobian, A., Peissig, P., Cross, D., Yale, S., & Craven, M. (2012). Learning to predict post-hospitalization VTE risk from EHR data. Unknown Journal, 2012, 436-445.