2026-09-20T01:01:59Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1513992023-08-01T03:08:24Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Ghassemi, Marzyeh
Yang, Ming Ying
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-07-31T19:36:49Z
2023-07-31T19:36:49Z
2023-06
2023-06-06T16:35:11.806Z
https://hdl.handle.net/1721.1/151399
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 indicating 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 enhance the predictive accuracy of a model, but they can improve the model’s calibration and fairness. We further 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.
M.Eng.
Massachusetts Institute of Technology
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Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Evaluating the Impact of Social Determinants of Health on
Prediction of Clinical Outcomes in the Intensive Care Unit
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<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 indicating 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 enhance the predictive accuracy of a model, but they can improve the model’s calibration and fairness. We further 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.</Abstract>
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