# Outlier detection for patient monitoring and alerting

arXiv: https://arxiv.org/abs/2605.08955 · HTML: https://mlsanity.com/paper/2605.08955/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 44 references that are in the map.
- First posted on arXiv 9 May 2026.

## Builds on (most cited first, 2 of 2)

- [Feature importance analysis for patient management decisions](https://mlsanity.com/paper/2605.04666/) (2026)
- [Conditional outlier detection for clinical alerting](https://mlsanity.com/paper/2605.05124/) (2026)

## Abstract (arXiv)

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.