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Intersectional consequences for marginal fairness in prediction models of emergency admissions

2024-11-05 health informatics Title + abstract only
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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 ...

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