# Unmasking Societal Biases in Respiratory Support for ICU Patients through Social Determinants of Health

arXiv: https://arxiv.org/abs/2502.16477 · HTML: https://mlsanity.com/paper/2502.16477/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 23 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Fairness Without Demographics in Repeated Loss Minimization](https://mlsanity.com/paper/1806.08010/) (2018)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III](https://mlsanity.com/paper/1907.08322/) (2019)
- [Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings](https://mlsanity.com/paper/2003.11515/) (2020)
- [An Extensive Data Processing Pipeline for MIMIC-IV](https://mlsanity.com/paper/2204.13841/) (2022)
- [Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning](https://mlsanity.com/paper/2210.02552/) (2022)
- [Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit](https://mlsanity.com/paper/2007.09483/) (2020)
- [Machine Learning for Mechanical Ventilation Control](https://mlsanity.com/paper/2102.06779/) (2021)
- [Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit](https://mlsanity.com/paper/2305.12622/) (2023)
- [Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities](https://mlsanity.com/paper/2403.18196/) (2024)

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

- [EchoQA: A Large Collection of Instruction Tuning Data for Echocardiogram Reports](https://mlsanity.com/paper/2503.02365/) (2025)

## Abstract (arXiv)

In critical care settings, where precise and timely interventions are crucial for health outcomes, evaluating disparities in patient outcomes is essential. Current approaches often fail to fully capture the impact of respiratory support interventions on individuals affected by social determinants of health. While attributes such as gender, race, and age are commonly assessed and provide valuable insights, they offer only a partial view of the complexities faced by diverse populations. In this study, we focus on two clinically motivated tasks: prolonged mechanical ventilation and successful weaning. Additionally, we conduct fairness audits on the models' predictions across demographic groups and social determinants of health to better understand health inequities in respiratory interventions within the intensive care unit. Furthermore, we release a temporal benchmark dataset, verified by clinical experts, to facilitate benchmarking of clinical respiratory intervention tasks.