A Fair Individualized Polysocial Risk Score for Identifying Increased Social Risk in Type 2 Diabetes
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Abstract
Background: Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications. It is crucial to implement effective social risk management strategies at the point of care. Objective: To develop an electronic health records (EHR)-based machine learning (ML) analytical pipeline to address unmet social needs associated with hospitalization risk in patients with T2D. Methods: We identified real-world patients with T2D from the EHR data from University of Florida (UF) Health Integrated Data Repository (IDR), incorporating both contextual SDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing instability). The 2015-2020 data were used for training and validation and 2021-2022 data for independent testing. We developed a machine learning analytic pipeline, namely individualized polysocial risk score (iPsRS), to identify high social risk associated with hospitalizations in T2D patients, along with explainable AI (XAI) and fairness optimization. Results: The study cohort included 10,192 real-world patients with T2D, with a mean age of 59 years and 58% female. Of the cohort, 50% were non-Hispanic White, 39% were non-Hispanic Black, 6% were Hispanic, and 5% were other races/ethnicities. Our iPsRS, including both contextual and individual-level SDoH as input factors, achieved a C statistic of 0.72 in predicting 1-year hospitalization after fairness optimization across racial and ethnic groups. The iPsRS showed excellent utility for capturing individuals at high hospitalization risk because of SDoH, that is, the actual 1-year hospitalization rate in the top 5% of iPsRS was 28.1%, ~13 times as high as the bottom decile (2.2% for 1-year hospitalization rate). Conclusion: Our ML pipeline iPsRS can fairly and accurately screen for patients who have increased social risk leading to hospitalization in real word patients with T2D.
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The top 5% risk score had a 28.1% hospitalization rate, approximately 13 times the bottom decile.
“C‐statistic 0.71; top 5% risk score had 28.1% hospitalization rate (~13x bottom decile); fairness optimization reduced bias without major accuracy loss”
From Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low‐Income Populations Within United States Health Systems: A Scoping Review (2026) · cites this paper as [21]
The Results section (page unavailable) reports a 27.1% one-year hospitalization rate for the top 10% of iPsRS scores, approximately 21 times the bottom-decile rate. It does not report the claimed 28.1% rate for the top 5% or the approximately 13-fold comparison. (LLM, conf=0.88)
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Experimental — accuracy not yet measuredPredictive ML modeling was the most common approach, used in 10 studies.
“Predictive ML modeling was the most common approach, used in 10 studies [21, 22, 23, 25, 26, 27, 30, 31, 32, 34].”
The individualized polysocial risk score for type 2 diabetes had a C-statistic of 0.71.
“C‐statistic 0.71; top 5% risk score had 28.1% hospitalization rate (~13x bottom decile); fairness optimization reduced bias without major accuracy loss”
Fairness optimization reduced bias without major accuracy loss.
“C‐statistic 0.71; top 5% risk score had 28.1% hospitalization rate (~13x bottom decile); fairness optimization reduced bias without major accuracy loss”
Huang et al. [21] reported a C-statistic of 0.71 with fairness-optimized social risk scoring in type 2 diabetes.
“Huang et al. [21] reported a C‐statistic of 0.71 with fairness‐optimized social risk scoring in type 2 diabetes”
Huang et al. [21] showed that fairness constraints can reduce disparate performance without substantially compromising overall predictive accuracy.
“showing that fairness constraints can reduce disparate performance without substantially compromising overall predictive accuracy”
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Integrating the Polysocial Risk Score: Enhancing Comprehensive Healthcare Delivery
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He, N., Wu, H., An, N., Bu, J., Li, Y., Dai, Y., Ou, Y., & He, F. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261435200 · Cited by 1 · see citing works
Brown, D. R., Cyr, D. D., Wruck, L. M., Stefano, T. A., Mehri, N., Bursac, Z., Munoz, R., Baum, M. K., Fluney, E., Bhoite, P., et al. (2025) · PLoS ONE · doi:10.1371/journal.pone.0328779 · Cited by 1 · see citing works
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Alfaqih, M., Haessner, P., LeLaurin, J. H., Guo, J., Hammer, N., Ike-Okpe, O. I., Pappa, M., He, X., Salloum, R. G., Bian, J., et al. (2026) · Applied Clinical Informatics · doi:10.1055/a-2917-6503
Self-citationAdaptation of a fair individualized polysocial risk score for hospitalization risk prediction
Gou, Q., Hu, Y., He, X., Gregory, M. E., LeLaurin, J. H., Salloum, R. G., Bian, J., Guo, J., & Huang, Y. (2026) · JAMIA Open · doi:10.1093/jamiaopen/ooag161
Self-citation
- Authors
- Huang, Y., Guo, J., Donahoo, W. T., Lee, Y. A., Fan, Z., Lu, Y., Chen, W.-H., Tang, H., Bilello, L., Saguil, A. A., Rosenberg, E., Shenkman, E. A., & Bian, J.
- Affiliations
- University of Florida Health (US)
- University of Florida (US)
- Corresponding author
- Bian, Jiang
- ORCID
- Venue
- Nature Communications
- Published
- 2024-10-05
- Volume
- 15
- Issue
- 1
- Pages
- 8653-8653
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11455957
- OpenAlex
- W4403146370
- Cited by
- 14
- References
- 75
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 5
- FWCI
- 2.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK133465)
- National Institute of Environmental Health Sciences (R24ES036131)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Health › Health disparities and outcomes
- Keywords
- type 2 diabetes, social determinants of health, hospitalization risk prediction, racial and ethnic disparities, algorithmic fairness, explainable AI, housing instability, neighborhood disadvantage, social needs screening, electronic health records
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