

























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.
| Comments: | AMIA 2010 Annual Symposium proceedings, pp. 286-290. Homer R. Warner Best Paper Award |
| Subjects: | Machine Learning (cs.LG); Computers and Society (cs.CY) |
| Cite as: | arXiv:2605.05124 [cs.LG] |
| (or arXiv:2605.05124v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.05124 arXiv-issued DOI via DataCite (pending registration) |
From: Michal Valko [view email]
[v1]
Wed, 6 May 2026 16:51:44 UTC (214 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。