预测癌症死亡率的公平性 跨种族子组的癌症死亡率
Teja Ganta1,2, Arash Kia3, Prathamesh Parchure4
1Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York.
JAMA network open
|July 10, 2024
概括
这项研究在预测癌症死亡风险的机器学习模型中没有发现种族偏见. 该模型在不同种族群体中展示了公平的表现,支持在癌症护理中的公平应用.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 健康 公平 卫生 公平
背景情况:
- 机器学习 (ML) 模型可以通过识别需要严重疾病对话的患者来帮助癌症治疗.
- 对种族偏见的ML模型的评估对于防止加剧现有的健康差异至关重要.
研究的目的:
- 在预测ML模型中评估种族偏见,该模型旨在识别180天癌症死亡风险.
- 确保该模型在不同种族群体之间具有公平的表现.
主要方法:
- 一项队列研究使用了随机森林算法与癌症注册表和EHR的回顾性数据.
- 该模型预测了2016年至2021年期间诊断的成人癌症患者的180天癌症死亡风险.
- 绩效和公平性指标 (例如,AUROC,F1得分,机会平等,赔率均等) 在种族类别中进行了评估.
主要成果:
- 验证数据集包括43,274名具有平衡人口特征的患者.
- 模型性能指标 (AUROC,F1得分) 在亚洲,黑人,美洲原住民,白人和其他/未知种族群体中显示出合理的一致性.
- 公平度量 (机会平等,赔率均等,影响差异) 显示没有显著变化,表明没有种族偏见.
结论:
- 癌症死亡风险的ML模型在各个种族群体中显示出公平的表现,表明没有明显的种族偏见.
- 这些发现支持这种模型具有公平临床应用的潜力.
- 建议在临床环境中进行持续监测,以确保持续公平的患者护理.
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