一个基于微模拟的框架,用于减轻初级保健数据中的社会偏见
medRxiv : the preprint server for health sciences
|December 11, 2025
概括
删除功能数据中的基于种族的调整可能会导致黑人个体的早期诊断,但不会影响预期寿命. 该框架有助于评估政策对健康数据的影响.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 医疗保健数据中的社会偏见可能会使不平等继续存在.
- 重新评估基于种族的标准需要了解它们对疾病进展的影响.
研究的目的:
- 提出一种新的微模拟框架,以减少初级保健数据中的社会偏见.
- 研究基于种族的标准对功能下降的影响.
主要方法:
- 开发了一种针对功能衰退 (eGFR) 的个体级仿真模型.
- 模拟的eGFR轨迹,包括高血压和糖尿病等因素.
- 应用贝叶斯校准来估计eGFR下降率.
主要成果:
- 在没有种族调整的情况下,黑人个人比调整后更早获得诊断资格.
- 这种差异在早期疾病阶段最为明显.
- 两种情景之间没有观察到预期寿命的显著差异.
结论:
- 数据转换框架可以通过表示数据生成过程来告知政策变化.
- 这种方法可以减轻各种健康数据集中的偏见.
- 显式建模数据生成是理解和解决偏见的关键.
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