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.
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AMIA Annual Symposium Proceedings, AMIA, 2010, 2010, pp.286-90
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Milos Hauskrecht, Michal Valko, Iyad Batal, Gilles Clermont, Shyam Visweswaran, et al.. Conditional Outlier Detection for Clinical Alerting. AMIA Annual Symposium Proceedings, AMIA, 2010, 2010, pp.286-90. 〈hal-00642993〉

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