使用异质的现实世界生存数据开发联合的时间到事件得分
Siqi Li1, Ziwen Wang1, Yuqing Shang1
1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.
Computers in biology and medicine
|September 21, 2025
概括
一个新的联合评分系统通过实现多站点协作而不会损害患者隐私,从而增强了生存分析. 这种保护隐私的框架可以提高对关键健康事件的预测准确度.
科学领域:
- 医疗保健分析 医疗保健分析
- 生物统计学 生物统计学
- 机器学习在医学中的应用
背景情况:
- 生存分析对于临床决策至关重要,可以预测时间到事件的结果.
- 目前的生存评分系统需要集中数据,由于隐私问题限制了多机构的研究.
- 联合学习提供了一个解决方案,可以在没有数据共享的情况下进行协作模式培训.
研究的目的:
- 开发一种新的,保护隐私的联合框架,用于构建生存评分系统.
- 为了实现多个数据所有者之间的高效和安全的协作,以预测生存结果.
- 解决现有的生存分数构建中单一来源数据假设的局限性.
主要方法:
- 一个联合的学习框架被设计为多站点的生存结果分析.
- 该方法应用于来自新加坡和美国急诊室的异质生存数据.
- 在每个参与地点独立开发本地生存得分以进行比较.
主要成果:
- 在所有测试地点,联合评分系统的表现始终优于本地模型.
- 接收器运行特征曲线 (iAUC) 下的集成面积在联合方法下显示了最大11.6%的改善.
- 与局部得分相比,联合得分显示出更高的依赖时间的AUC (t) 值,信心区间较窄.
结论:
- 拟议的联合生存得分生成框架是有效的,适用于现实世界的异质数据.
- 这种保护隐私的方法提高了生存模型的预测准确性和效率.
- 该框架对未来的协作医疗保健研究具有前景,改善了临床环境中的风险预测.
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