在数字患者双胞胎中实现人口 robustness:解决性别数据差距
Dana Mahr1, Meike Hebich2, Nora Weinberger1
1Institute for Technology Assessment and Systems Analysis, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Digital health
|November 6, 2025
概括
数字患者双胞胎需要基于性别和性别的公平,以确保公平和准确性. 当前的模型往往低于不同人群的代表性,导致精准医学中的健康不平等.
科学领域:
- 数字健康数字健康
- 生物医学信息学是生物医学信息学.
- 卫生公平性健康公平性
背景情况:
- 数字患者双胞胎 (DPT) 是精准医学的新兴工具,模拟个人健康.
- 它们的有效性和公平性取决于代表性数据和公正的算法.
- 现有的DPT举措经常表现出偏见,过度代表某些人口统计数据.
研究的目的:
- 争取性别和基于性别的平等作为DPT设计和实施的核心原则.
- 突出目前的DPT如何延续健康差异,原因是多元人口的代表性不足.
- 为创造公平和临床有效的DPT提出解决方案.
主要方法:
- 这篇意见稿综合了心脏病学,内分泌学,心理健康和医疗器械研究的证据.
- 它分析了与DPT相关的研究和应用中存在偏见的记录实例.
- 作者借鉴了临床研究中结构性偏差的已知知识.
主要成果:
- 目前的DPT举措不成比例地代表了男性,白人和富裕人口.
- 妇女,性别多样化个体和有色人种的代表性不足,导致误诊和不准确.
- 例如,女性对心力衰竭的认知不足,以及对黑色皮肤颜色的女性过度估计氧和度.
结论:
- 临床研究中的结构性偏见有助于DPT不平等.
- 解决这些问题需要均衡的数据,以性别和性别为基础的设计,参与式方法和子组验证.
- 在DPT中公平是必不可少的,以避免加剧现有的健康差异,并实现个性化医学的潜力.
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