Evaluating the Impact of Social Determinants of Health on
Prediction of Clinical Outcomes in the Intensive Care Unit
Name
yang-ming1022-meng-eecs-2023-thesis.pdf
Description
Thesis PDF
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2.11 MB
Format
Adobe PDF
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1c544fd593ad2bf8123750390a9fee3c
Author(s)
Yang, Ming Ying
Advisor(s)
Ghassemi, Marzyeh
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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