# Conditional outlier detection for clinical alerting

arXiv: https://arxiv.org/abs/2605.05124 · HTML: https://mlsanity.com/paper/2605.05124/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 14 references that are in the map.
- First posted on arXiv 6 May 2026.

## Built on by (most cited first, 1 of 1)

- [Outlier detection for patient monitoring and alerting](https://mlsanity.com/paper/2605.08955/) (2026)

## Abstract (arXiv)

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.