Conditional outlier detection for clinical alerting

Hauskrecht M, Valko M, Batal I, Clermont G, Visweswaran S, Cooper GF. Conditional outlier detection for clinical alerting. In: Proceedings of the Annual Symposium of the American Medical Informatics Association (2010) 286-290. PMID: 21346986 PMC3041310

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.

Publication Year: 
2010
Publication Credits: 
Hauskrecht M, Valko M, Batal I, Clermont G, Visweswaran S, Cooper GF.
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