title: Learning to predict post-hospitalization VTE risk from EHR data.
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.
authors.affiliation: University of Wisconsin-Madison, Madison, WI, USA.
authors.forename: Emily
authors.initials: E
authors.lastname: Kawaler
authors.forename: Alexander
authors.initials: A
authors.lastname: Cobian
authors.forename: Peggy
authors.initials: P
authors.lastname: Peissig
authors.forename: Deanna
authors.initials: D
authors.lastname: Cross
authors.forename: Steve
authors.initials: S
authors.lastname: Yale
authors.forename: Mark
authors.initials: M
authors.lastname: Craven
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D057286:Electronic Health Records / Q000145:classification; D006760:Hospitalization; D006801:Humans; D008875:Middle Aged; D020641:Polymorphism, Single Nucleotide; D011237:Predictive Value of Tests; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D016019:Survival Analysis; D054556:Venous Thromboembolism