Intersectional consequences for marginal fairness in prediction models of emergency admissions

BackgroundFair clinical prediction models are crucial for achieving equitable health outcomes. Recently, intersectionality has been applied to develop fairness algorithms that address discrimination among intersections of protected attributes (e.g., Black women rather than Black persons or women separately). Still, the majority of medical AI literature applies marginal de-biasing approaches, which constrain performance across one or many isolated patient attributes. We investigate the extent to which this modeling decision affects model equity and performance in a well-defined use case in emergency medicine. MethodsThe study focused on predicting emergency room admissions using electronic health record data from two large U.S. hospitals, Beth Israel Deaconess Medical Center (MIMIC-IV-ED, n=160,016) and Boston Childrens Hospital (BCH, n=22,222), covering both adult and pediatric populations. In a comprehensive experiment over fairness definitions, modeling methods, we compared the performance of single- and multi-attribute, marginal de-biasing approaches to intersectional de-biasing approaches. ResultsIntersectional de-biasing produces greater reductions in subgroup calibration error (MIMIC- IV: 21.2%; BCH: 27.2%) than marginal de-biasing (MIMIC-IV: 10.6%; BCH: 22.7%), and also lowers subgroup false negative rates on MIMIC-IV an additional 3.5% relative to marginal de-biasing. These fairness gains were achieved without a significant decrease in model accuracy between baseline and intersectionally-debiased models (MIMIC-IV: AUROC=0.85{+/-}0.00, both models; BCH: AUROC=0.88{+/-}0.01 vs 0.87{+/-}0.01). Intersectional de-biasing more effectively lowered subgroup calibration error and FNRs in low-prevalence groups in both datasets compared to other de-biasing conditions. ConclusionIntersectional de-biasing better mitigates performance disparities across intersecting groups compared to marginal approaches for emergency admission prediction. These strategies meaningfully reduce group-specific error rates without compromising overall accuracy. These findings highlight the importance of considering interacting aspects of patient identity in model development, and suggest that intersectional de-biasing would be a promising gold standard for ensuring equity in clinical prediction models.