开发公平的个体化多维社会风险评分（iPsRS），用于识别2型糖尿病（T2D）患者住院风险增加的社会风险
Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)
AI总结：
本研究开发了基于电子健康记录的机器学习管道iPsRS，结合可解释AI与公平性优化，准确且公平地识别2型糖尿病患者因社会风险增加导致的住院风险。
摘要：
背景：种族和少数民族群体以及面临社会劣势的个体（通常源于其健康的社会决定因素（SDoH））承担了不成比例的2型糖尿病（T2D）及其并发症的负担。因此，在就诊时实施有效的社会风险管理策略至关重要。目的：开发基于EHR的机器学习（ML）分析管道，以识别T2D患者中与住院风险相关的未满足的社会需求。方法：我们从University of Florida Health Integrated Data Repository的EHR数据（2012年至2022年）中识别了10,192名T2D患者，包括情境性SDoH（如社区剥夺）和个体层面SDoH（如住房稳定性）。我们开发了一种基于电子健康记录（EHR）的机器学习（ML）分析管道，即个体化多维社会风险评分（iPsRS），以识别T2D患者中与住院相关的高社会风险，并结合了可解释人工智能（XAI）技术以及公平性评估与优化。结果：在跨种族-民族群体进行公平性优化后，我们的iPsRS在预测1年住院率方面达到了0.72的C统计量。iPsRS在识别高住院风险个体方面表现出极佳的效用；iPsRS前5%人群的实际1年住院率约为最低十分位人群的13倍。结论：我们的机器学习管道iPsRS能够公平且准确地筛查出社会风险增加并导致T2D患者住院的患者。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
