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

• Published in: arXiv.org 2023-08
• Main Authors: Yang, Ming Ying, Kwak, Gloria Hyunjung, Pollard, Tom, Celi, Leo Anthony, Ghassemi, Marzyeh
• Format: Article
• Language: English
• Subjects: Algorithms; Bias; Electronic health records; Prediction models
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2305.12622

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.