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- W4385848286 abstract "Social determinants of health (SDOH) -- the conditions in which people live, grow, and age -- play a crucial role in a person's health and well-being. There is a large, compelling body of evidence in population health studies showing that a wide range of SDOH is strongly correlated with health outcomes. Yet, a majority of the risk prediction models based on electronic health records (EHR) do not incorporate a comprehensive set of SDOH features as they are often noisy or simply unavailable. Our work links a publicly available EHR database, MIMIC-IV, to well-documented SDOH features. We investigate the impact of such features on common EHR prediction tasks across different patient populations. We find that community-level SDOH features do not improve model performance for a general patient population, but can improve data-limited model fairness for specific subpopulations. We also demonstrate that SDOH features are vital for conducting thorough audits of algorithmic biases beyond protective attributes. We hope the new integrated EHR-SDOH database will enable studies on the relationship between community health and individual outcomes and provide new benchmarks to study algorithmic biases beyond race, gender, and age." @default.
- W4385848286 created "2023-08-16" @default.
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- W4385848286 date "2023-08-08" @default.
- W4385848286 modified "2023-10-01" @default.
- W4385848286 title "Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit" @default.
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- W4385848286 doi "https://doi.org/10.1145/3600211.3604719" @default.
- W4385848286 hasPublicationYear "2023" @default.
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