一个公平的个性化多社会风险评分,用于识别2型糖尿病的社会风险增加
Yu Huang1, Jingchuan Guo2, William T Donahoo3
1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
Nature communications
|October 5, 2024
概括
这项研究引入了一种人工智能工具,用于识别因社会因素而面临住院住院高风险的2型糖尿病患者. 个性化多社会风险评分 (iPsRS) 可以合理预测住院情况,解决健康差异.
科学领域:
- 健康差距 研究 研究 研究 研究
- 医疗保健中的人工智能
- 健康的社会决定因素 (SDoH) 分析
背景情况:
- 种族和少数民族不成比例地患有2型糖尿病 (T2D) 和其并发症.
- 健康的社会决定因素 (SDoH) 是这些健康差异的主要驱动因素.
- 针对社会需求的有效管理策略对于减少T2D并发症至关重要.
研究的目的:
- 开发和验证用于计算个性化多社会风险评分 (iPsRS) 的机器学习管道.
- 根据SDoH,识别高风险住院的T2D患者.
- 将可解释的AI和算法公平性纳入公平的风险评估.
主要方法:
- 利用了来自T2D患者的电子健康记录 (EHR) 数据.
- 整合了上下文的SDoH (例如,邻里贫困) 和个人层面的SDoH (例如,住房不稳定).
- 针对iPSRS模型,应用了跨种族和民族群体的公平性优化.
主要成果:
- 在公平性优化后,iPSRS在预测1年住院病例时实现了0.71的C统计.
- 该模型展示了社会风险的T2D患者公平而准确的查能力.
- 确定了潜在住院的高社会风险患者.
结论:
- 开发的iPSRS是一种公平而准确的工具,用于查患有住院风险增加的T2D患者.
- 这种方法可以帮助缓解由健康的社会决定因素驱动的健康差异.
- 为高风险人群提供有针对性的干预措施.
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