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.

翻译：背景：种族和族裔少数群体以及面临社会不利因素（通常源于其健康社会决定因素）的个体，承担着2型糖尿病及其并发症的过高负担。因此，在诊疗点实施有效的社会风险管理策略至关重要。目的：开发基于电子健康记录的机器学习分析流程，识别与2型糖尿病患者住院风险相关的未满足社会需求。方法：我们从佛罗里达大学健康综合数据仓库的电子健康记录数据（2012-2022年）中筛选出10,192例2型糖尿病患者，纳入情境性健康社会决定因素（如邻里剥夺）和个体层面健康社会决定因素（如住房稳定性）。我们开发了基于电子健康记录的机器学习分析流程，即个体化多社会风险评分，用于识别与2型糖尿病患者住院相关的高社会风险，并结合可解释人工智能技术及公平性评估与优化。结果：经种族-族裔群体公平性优化后，我们的个体化多社会风险评分在预测1年住院风险时C统计量达0.72。该评分在识别高住院风险个体方面展现出卓越效能：个体化多社会风险评分前5%患者的实际1年住院率约为后十分位数组的13倍。结论：我们的机器学习流程个体化多社会风险评分能够公平且准确地筛查2型糖尿病患者中因社会风险增加导致住院的高危人群。