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cs.LG updates on arXiv.org

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Outlier detection for patient monitoring and alerting
Milo\v{s} Ha · 2026-05-12 · via cs.LG updates on arXiv.org

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Abstract:We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). Our hypothesis is that a patient-management decision that is unusual with respect to past patient care may be due to an error and that it is worthwhile to generate an alert if such a decision is encountered. We evaluate this hypothesis using data obtained from EHRs of 4486 post-cardiac surgical patients and a subset of 222 alerts generated from the data. We base the evaluation on the opinions of a panel of experts. The results of the study support our hypothesis that the outlier-based alerting can lead to promising true alert rates. We observed true alert rates that ranged from 25\% to 66\% for a variety of patient-management actions, with 66\% corresponding to the strongest outliers.
Comments: Published at JBI 2013
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2605.08955 [cs.LG]
  (or arXiv:2605.08955v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2605.08955

arXiv-issued DOI via DataCite (pending registration)

Related DOI: https://doi.org/10.1016/j.jbi.2012.08.004

DOI(s) linking to related resources

Submission history

From: Michal Valko [view email]
[v1] Sat, 9 May 2026 13:52:15 UTC (723 KB)