临床算法中的种族调整可以帮助纠正数据质量的种族差异
Anna Zink1, Ziad Obermeyer2, Emma Pierson3,4
1Booth School of Business, University of Chicago, Chicago, IL 60637.
概括
临床算法中的种族调整可以通过计算数据质量差异来改善黑人癌症风险预测. 这种方法可以增强对预测不足的群体的查访问.
科学领域:
- 卫生公平性健康公平性
- 临床信息学是一种临床信息学.
- 流行病学 流行病学
背景情况:
- 在临床算法中围绕种族进行的伦理辩论,但种族数据质量差异往往被忽视.
- 预测算法依赖于输入特征,其文档质量在种族群体之间可能有所不同,影响准确性.
研究的目的:
- 评估结直肠癌风险预测模型中的种族调整是否可以解决种族群体之间的数据质量差异.
- 评估包括种族在算法中的影响,当关键预测因素具有差异性数据质量时.
主要方法:
- 利用了来自南方社区队列研究的数据 (77,836名没有先前结直肠癌的成年人).
- 将种族盲算法与种族调整算法进行比较,用于结直肠癌风险预测.
- 使用合适度和接收器运行特征曲线下的面积来评估预测性能.
主要成果:
- 结直肠癌家族病史的预测价值在黑人参与者中低于白人参与者.
- 与种族盲算法相比,种族调整算法显示出更好的预测性能,特别是对于黑人参与者 (P < 0.001,P = 0.006).
- 根据种族调整的模型纠正了对黑人参与者的风险预测不足,从而有可能改善查分配.
结论:
- 当输入特征数据质量因种族而异时,临床算法中的种族调整可能是有益的.
- 将种族纳入算法可能会减轻因数据质量差异而产生的偏见,提高风险预测和医疗保健准入方面的公平性.
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