CYAILGFeb 23, 2025

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

arXiv:2502.16477v14 citationsh-index: 10IJCAI
Originality Synthesis-oriented
AI Analysis

This work addresses health inequities in critical care for diverse patient populations, though it is incremental in applying existing methods to new data.

The study tackled disparities in respiratory support outcomes for ICU patients by analyzing prolonged mechanical ventilation and successful weaning, conducting fairness audits across demographic groups and social determinants of health, and releasing a verified temporal benchmark dataset.

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.

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