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

Yu Huang, Jingchuan Guo, William T. Donahoo, Yao An Lee, Zhengkang Fan, Ying Lu, Wei-Han Chen, Huilin Tang, Lori Bilello, Aaron A. Saguil, Eric Rosenberg, Elizabeth A. Shenkman and Jiang Bian ()
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Yu Huang: University of Florida
Jingchuan Guo: University of Florida
William T. Donahoo: University of Florida
Yao An Lee: University of Florida
Zhengkang Fan: University of Florida
Ying Lu: University of Florida
Wei-Han Chen: University of Florida
Huilin Tang: University of Florida
Lori Bilello: University of Florida
Aaron A. Saguil: University of Florida
Eric Rosenberg: University of Florida
Elizabeth A. Shenkman: University of Florida
Jiang Bian: University of Florida

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-52960-9

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