公平的个体化多社会风险评分,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.
Research square
|December 18, 2023
概括
一种新的机器学习模型,即个性化多社会风险评分 (iPsRS),可以公平地识别因健康的社会决定因素 (SDoH) 而面临住院高风险的2型糖尿病 (T2D) 患者. 这种工具有助于管理在护理地点的社会风险.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 健康研究的社会决定因素研究
背景情况:
- 种族和少数民族群体不成比例地患有2型糖尿病 (T2D) 和其并发症,通常与健康的社会决定因素 (SDoH) 相关.
- 在护理点对社会风险的有效管理对于减轻这些健康差异至关重要.
研究的目的:
- 开发基于电子健康记录 (EHR) 的机器学习 (ML) 分析管道,以确定导致T2D患者住院风险的未满足的社会需求.
- 创建一个公平而准确的工具来查T2D患者的高社会风险住院.
主要方法:
- 利用来自佛罗里达大学健康综合数据库 (IDR) 的10,192名T2D患者 (2015-2022) 的EHR数据.
- 开发了一个ML管道,个性化多社会风险评分 (iPsRS),结合了上下文和个人级别的SDoH.
- 应用可解释的人工智能 (XAI) 和公平性优化技术,以进行公平的风险评估.
主要成果:
- 在公平性优化后,iPSRS在预测1年的住院治疗时达到0.72的C统计.
- 通过iPSRS识别的前5%的患者有28.1%的1年住院率,大约是底部十分位数的13倍.
- 该模型在捕获不同种族和种族群体中因SDoH而面临高风险的个体方面表现出实用性.
结论:
- 由于SDoH,iPsRS ML管道提供了一种公平而准确的方法来查T2D患者,因为SDoH而增加住院的风险.
- 这种方法为高风险个体提供了有针对性的干预措施,在现实临床环境中解决未得到满足的社会需求.
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