Medicine and Health

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

Y. Huang, J. Guo, et al.

Discover how researchers from the University of Florida developed an innovative machine learning pipeline to create an individualized polysocial risk score for type 2 diabetes patients. This groundbreaking study addresses the challenges faced by racial and ethnic minorities, showcasing an effective tool for predicting hospitalization risks with a focus on social determinants of health.

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Playback language: English

Index

Abstract

Racial and ethnic minorities disproportionately experience type 2 diabetes (T2D) and its complications, influenced heavily by social determinants of health (SDOH). This study proposes a machine learning pipeline to create an individualized polysocial risk score (iPsRS) for identifying T2D patients at high social risk of hospitalization. Using electronic health records (EHR) data, the iPsRS incorporates both contextual and person-level SDOH, with fairness optimization across racial and ethnic groups. After optimization, the iPsRS achieved a C-statistic of 0.71 in predicting 1-year hospitalization, demonstrating its ability to fairly and accurately screen for increased social risk in T2D patients.

Publisher

Nature Communications

Published On

Oct 05, 2024

Authors

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, Jiang Bian

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