mlsanityData to 14 Oct 2026

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

arXiv 2305.12622 ↗First posted 22 May 2023cs.LG cs.CYcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 14 of its 148 references that are in the map. First posted on arXiv 22 May 2023.

Builds on

14 of 148 references.

References · 14

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  3. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 364
  4. 2017Multitask learning and benchmarking with clinical time series datacited by 290
  5. 2020CheXclusion: Fairness gaps in deep chest X-ray classifierscited by 153
  6. 2018Why Is My Classifier Discriminatory?cited by 143
  7. 2019Towards a Critical Race Methodology in Algorithmic Fairnesscited by 125
  8. 2020Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddingscited by 79
  9. 2022Improving the Fairness of Chest X-ray Classifierscited by 52
  10. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  11. 2018Creating Fair Models of Atherosclerotic Cardiovascular Disease Riskcited by 23
  12. 2022Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendationscited by 20
2 more
  1. 2022SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) predictioncited by 13
  2. 2016Neural Document Embeddings for Intensive Care Patient Mortality Predictioncited by 7

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Cited by year

Citing papers per year: 2024: 4; 2025: 3.

7 papers.

  1. 2024Examining Imbalance Effects on Performance and Demographic Fairness of Clinical Language Modelscited by 4
  2. 2024Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequitiescited by 3
  3. 2025Critical Appraisal of Fairness Metrics in Clinical Predictive AIcited by 2
  4. 2024To which reference class do you belong? Measuring racial fairness of reference classes with normative modelingcited by 1
  5. 2025Unmasking Societal Biases in Respiratory Support for ICU Patients through Social Determinants of Healthcited by 1
  6. 2025EchoQA: A Large Collection of Instruction Tuning Data for Echocardiogram Reportscited by 1
  7. 2024Large Language Models for Integrating Social Determinant of Health Data: A Case Study on Heart Failure 30-Day Readmission Predictioncited by 0