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通过公平意识的机器学习模型缓解前列腺癌生存预测差异.

Hyungrok Do1, Rajesh Ranganath2,3, Katie Murray4,5

  • 1Department of Population Health, New York University School of Medicine, New York, New York, USA.

Cancer medicine
|January 27, 2026
PubMed
概括
此摘要是机器生成的。

公平意识的生存模型可以减少前列腺癌生存预测中的种族差异. 这些深度学习模型提高了不同人口群体的准确性,促进了公平的医疗保健.

关键词:
偏见 偏见 偏见 偏见 偏见公平的公平的公平.机器学习是机器学习.前列腺癌是前列腺癌.幸存率 幸存率 生存率

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科学领域:

  • 在瘤学瘤学.
  • 生物统计学 生物统计学
  • 机器学习 机器学习

背景情况:

  • 预测模型可以使医疗保健差异持续存在,原因是人口群体的表现不平等.
  • 公平意识的方法是建立在二元结果的,但在生存分析中不太探索.
  • 在预测前列腺癌治疗后的生存率方面存在种族差异.

研究的目的:

  • 为了比较两个公平意识的深度学习生存模型,以预测激进前列腺切除术后的生存率.
  • 在这些预测模型的性能中减轻种族差异.
  • 评估公平意识方法在前列腺癌生存率分析中的有效性.

主要方法:

  • 利用国家癌症数据库来训练对整体存活的深度考克斯比例危险模型.
  • 我们比较了两个以公平意识为基础的模型:公平的深考克斯比例危险模型 (Fair DCPH) 和集团分布稳健优化深考克斯比例危险模型 (GroupDRO DCPH).
  • 通过跨族群和跨族群一致性指数 (C指数) 评估模型的公平性.

主要成果:

  • 基线深考克斯比例危险模型 (DCPH) 显示了种族群体之间的绩效差异 (例如,黑人和西班牙裔患者的C指数较低).
  • 公平意识模型 (Fair DCPH,GroupDRO DCPH) 改善了黑人,西班牙裔和亚洲患者的跨组C指数.
  • 在白色患者小组中,这些改进是在最小的性能损失下实现的.

结论:

  • 两种公平意识的生存模型进行了基准测试,以解决前列腺切除术后生存预测中的种族差异.
  • 这些方法表明,通过公平的预测模型,可以确保公平的护理.
  • 这些方法可以扩展到其他时间到事件模型,以促进医疗预测的公平性.