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探索在公平的中风风险预测中与平价受约束和无种族模型进行权衡
Matthew Engelhard1, Daniel Wojdyla2, Haoyuan Wang3
1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, United States of America; Duke AI Health, United States of America.
Artificial intelligence in medicine
|April 20, 2025
概括
标准机器学习模型显示,黑人和白人之间中风风险预测的表现差异. 这项研究比较了无种族和受平等限制的模型来解决这些不平等问题,揭示了校准和歧视之间的权衡.
科学领域:
- 临床信息学是一种临床信息学.
- 机器学习在医疗保健中的应用
- 卫生公平研究 卫生公平研究
背景情况:
- 现有的中风风险预测模型显示了种族子组之间的绩效差异.
- 标准的机器学习方法并没有解决这些不平等问题.
- 呼叫存在于消除种族作为预测因素 (无种族模型) 或限制群体之间的预测差异.
研究的目的:
- 为了比较无种族和同等限制的机器学习模型的有效性,以公平地预测中风风险.
- 为了评估一个新的平价受约束的神经网络时间到事件模型.
主要方法:
- 开发了对等性受约束和不受约束的神经网络事件时间模型.
- 利用了来自Framingham Offspring,MESA和ARIC研究的协调数据.
- 与使用持久测试和验证集 (REGARDS) 的无种族模型进行模型性能比较,对正确审查的结果进行组内和组间指标的评估.
主要成果:
- 同等性受限制的模型牺牲了集团内部校准,以改善集团内部的歧视.
- 无种族模型在集团内部校准和集团内部歧视之间取得了平衡.
- 在模型校准和基于使用的方法的歧视之间存在着根本的权衡.
结论:
- 对中风风险预测的最佳模型选择取决于预期的临床应用及其相关的益处和危害.
- 结果为开发公平的临床风险预测模型提供了一个框架.
- 突出了在临床风险预测中无种族方法的优点和局限性.
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