{"type":"ScholarlyArticle","title":"Unmasking Societal Biases in Respiratory Support for ICU Patients through Social Determinants of Health","authors":[{"name":"Mira Moukheiber","slug":"mira-moukheiber","given":"Mira","family":"Moukheiber","affiliation":"Massachusetts Institute of Technology"},{"name":"Lama Moukheiber","slug":"lama-moukheiber","given":"Lama","family":"Moukheiber","affiliation":"Massachusetts Institute of Technology"},{"name":"Dana Moukheiber","slug":"dana-moukheiber","given":"Dana","family":"Moukheiber","affiliation":"Massachusetts Institute of Technology"},{"name":"Hyung-Chul Lee","slug":"hyung-chul-lee","given":"Hyung-Chul","family":"Lee","affiliation":"Seoul National University College of Medicine , Seoul National University Hospital , Department of Anesthesiology and Pain Medicine"}],"abstract":"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.","doi":"10.24963/ijcai.2024/821","url":"https://ijcai.org/proceedings/2024/821","pdf":"https://ijcai.org/proceedings/2024/0821.pdf","markdown":"https://ijcai.org/proceedings/2024/821.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/821","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":821,"pages":{"first":7421,"last":7429},"keywords":["AI Ethics, Trust, Fairness: General","Data Mining: General","Machine Learning: General"],"videos":[],"references":[{"position":1,"raw":"[Bundy et al., 2023] Joshua D Bundy, Katherine T Mills, Hua He, Thomas A LaVeist, Keith C Ferdinand, Jing Chen, and Jiang He. 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