@article{hauskrecht2010conditional,
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.},
author = {Hauskrecht, Milos and Valko, Michal and Batal, Iyad and Clermont, Gilles and Visweswaran, Shyam and Cooper, Gregory F},
journal = {Annual American Medical Informatics Association Symposium},
keywords = {misovalko},
mendeley-tags = {misovalko},
title = {{Conditional Outlier Detection for Clinical Alerting}},
year = {2010}
}