# Conditional anomaly detection using soft harmonic functions: An application to clinical alerting

arXiv: https://arxiv.org/abs/2604.21956 · HTML: https://mlsanity.com/paper/2604.21956/

## 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 23 Apr 2026.

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

- [Adaptive graph-based algorithms for conditional anomaly detection and semi-supervised learning](https://mlsanity.com/paper/2605.03495/) (2026)

## Abstract (arXiv)

Timely detection of concerning events is an important problem in clinical practice. In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response, such as the omission of an important lab test. We develop a new non-parametric approach for conditional anomaly detection based on the soft harmonic solution, with which we estimate the confidence of the label to detect anomalous mislabeling. We further regularize the solution to avoid the detection of isolated examples and examples on the boundary of the distribution support. We demonstrate the efficacy of the proposed method in detecting unusual labels on a real-world electronic health record dataset and compare it to several baseline approaches.