# Monitoring fairness in machine learning models that predict patient mortality in the ICU

arXiv: https://arxiv.org/abs/2411.00190 · HTML: https://mlsanity.com/paper/2411.00190/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Built on by (most cited first, 2 of 2)

- [Improving Fairness of Large Language Model-Based ICU Mortality Prediction via Case-Based Prompting](https://mlsanity.com/paper/2512.19735/) (2025)
- [Metamorphic Testing for Clinical ML Models: A Framework Proposal and Pilot Study](https://mlsanity.com/paper/2607.22984/) (2026)

## Abstract (arXiv)

This work proposes a fairness monitoring approach for machine learning models that predict patient mortality in the ICU. We investigate how well models perform for patient groups with different race, sex and medical diagnoses. We investigate Documentation bias in clinical measurement, showing how fairness analysis provides a more detailed and insightful comparison of model performance than traditional accuracy metrics alone.