2010 ConditionalOutlierDetectionforC

• (Hauskrecht et al., 2010) ⇒ Milos Hauskrecht, Michal Valko, Iyad Batal, Gilles Clermont, Shyam Visweswaran, and Gregory Cooper. (2010). “Conditional Outlier Detection for Clinical Alerting.” In: AMIA annual symposium proceedings.

Subject Headings: Outlier Detection.

Cited By

• http://scholar.google.com/scholar?q=%222010%22+Conditional+Outlier+Detection+for+Clinical+Alerting

Quotes

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 ConditionalOutlierDetectionforC	Iyad Batal Milos Hauskrecht Michal Valko Shyam Visweswaran Gilles Clermont Gregory Cooper			Conditional Outlier Detection for Clinical Alerting						2010