Learning to Predict Post-hospitalization VTE Risk from EHR Data

Overview

Journal AMIA Annu Symp Proc

Specialty Medical Informatics

Date 2013 Jan 11

PMID 23304314

Citations 27

Authors

Emily Kawaler

Alexander Cobian

Peggy Peissig

Deanna Cross

Steve Yale

Mark Craven

Affiliations

Soon will be listed here.

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.

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