Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit

• URL: http://arxiv.org/abs/2305.12622v2
• Date: Mon, 14 Aug 2023 16:49:03 GMT
• Title: Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit
• Authors: Ming Ying Yang, Gloria Hyunjung Kwak, Tom Pollard, Leo Anthony Celi, and Marzyeh Ghassemi
• Abstract summary: Social determinants of health (SDOH) play a crucial role in a person's health and well-being. Most risk prediction models based on electronic health records do not incorporate a comprehensive set of SDOH features. Our work links a publicly available EHR database, MIMIC-IV, to well-documented SDOH features.
• Score: 10.764842579064636
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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