title: Conditional outlier detection for clinical alerting.
abstract: We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electronic health record (EHR) system. Our hypothesis is that patient-management actions that are unusual with respect to past patients may be due to a potential error and that it is worthwhile to raise an alert if such a condition is encountered. We evaluate this hypothesis using data obtained from the electronic health records of 4,486 post-cardiac surgical patients. We base the evaluation on the opinions of a panel of experts. The results support that anomaly-based alerting can have reasonably low false alert rates and that stronger anomalies are correlated with higher alert rates.
authors.affiliation: Computer Science Department.
authors.forename: Milos
authors.initials: M
authors.lastname: Hauskrecht
authors.forename: Michal
authors.initials: M
authors.lastname: Valko
authors.forename: Iyad
authors.initials: I
authors.lastname: Batal
authors.forename: Gilles
authors.initials: G
authors.lastname: Clermont
authors.forename: Shyam
authors.initials: S
authors.lastname: Visweswaran
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
mesh_terms: D057286:Electronic Health Records; D006801:Humans