Developing A Fair Individualized Polysocial Risk Score (iPsRS) for
  Identifying Increased Social Risk of Hospitalizations in Patients with Type 2
  Diabetes (T2D)
Yu Huang Jingchuan Guo William T Donahoo Zhengkang Fan Ying Lu Wei-Han Chen Huilin Tang Lori Bilello Elizabeth A Shenkman Jiang Bian
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 therefore crucial to implement effective social risk
management strategies at the point of care. Objective: To develop an EHR-based
machine learning (ML) analytical pipeline to identify the unmet social needs
associated with hospitalization risk in patients with T2D. Methods: We
identified 10,192 T2D patients from the EHR data (from 2012 to 2022) from the
University of Florida Health Integrated Data Repository, including contextual
SDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing
stability). We developed an electronic health records (EHR)-based machine
learning (ML) analytic pipeline, namely individualized polysocial risk score
(iPsRS), to identify high social risk associated with hospitalizations in T2D
patients, along with explainable AI (XAI) techniques and fairness assessment
and optimization. Results: Our iPsRS achieved a C statistic of 0.72 in
predicting 1-year hospitalization after fairness optimization across
racial-ethnic groups. The iPsRS showed excellent utility for capturing
individuals at high hospitalization risk; the actual 1-year hospitalization
rate in the top 5% of iPsRS was ~13 times as high as the bottom decile.
Conclusion: Our ML pipeline iPsRS can fairly and accurately screen for patients
who have increased social risk leading to hospitalization in T2D patients.