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

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

Mira Moukheiber, Lama Moukheiber, Dana Moukheiber, Hyung-Chul Lee

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
AI for Good. Pages 7421-7429. https://doi.org/10.24963/ijcai.2024/821

In critical care settings, where precise and timely interventions are crucial for health outcomes, evaluating disparities in patient outcomes is important. Current approaches often fall short in comprehensively understanding and evaluating the impact of respiratory support interventions on individuals affected by social determinants of health. Attributes such as gender, race, and age are commonly assessed and essential, but provide 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. We also perform fairness audits on the models' predictions across demographic groups and social determinants of health to better understand the health inequities in respiratory interventions in the intensive care unit. We also release a temporal benchmark dataset, verified by clinical experts, to enable benchmarking of clinical respiratory intervention tasks.
Keywords:
AI Ethics, Trust, Fairness: General
Data Mining: General
Machine Learning: General