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A fair individualized polysocial risk score for identifying increased social risk in type 2 diabetes

Affiliations
  • 1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
  • 2Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA.
  • 3Division of Endocrinology, Diabetes and Metabolism, College of Medicine, University of Florida, Gainesville, FL, USA.
  • 4Department of Surgery, College of Medicine- Jacksonville, University of Florida, Jacksonville, FL, USA.
  • 5Department of Community Health and Family Medicine, College of Medicine, University of Florida, Jacksonville, FL, USA.
  • 6Division of General Internal Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA.
  • 7Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA. bianjiang@ufl.edu.

Published on:

October 5, 2024

Abstract

Racial and ethnic minorities bear a disproportionate burden of type 2 diabetes (T2D) and its complications, with social determinants of health (SDoH) recognized as key drivers of these disparities. Implementing efficient and effective social needs management strategies is crucial. We propose a machine learning analytic pipeline to calculate the individualized polysocial risk score (iPsRS), which can identify T2D patients at high social risk for hospitalization, incorporating explainable AI techniques and algorithmic fairness optimization. We use electronic health records (EHR) data from T2D patients in the University of Florida Health Integrated Data Repository, incorporating both contextual SDoH (e.g., neighborhood deprivation) and person-level SDoH (e.g., housing instability). After fairness optimization across racial and ethnic groups, the iPsRS achieved a C statistic of 0.71 in predicting 1-year hospitalization. Our iPsRS can fairly and accurately screen patients with T2D who are at increased social risk for hospitalization.

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