开发使用联合学习的时间到事件预测模型
Rasmus Rask Kragh Jørgensen1,2,3, Jonas Faartoft Jensen4, Tarec El-Galaly4,5,6,7
1Department of Hematology, Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark. Rasmus.rask@rn.dk.
BMC medical research methodology
|May 26, 2025
概括
联合学习 (FL) 能够在多个地点训练生存预测模型,而无需共享敏感的患者数据. 这种方法保持了与传统集中方法相比的预测性能,促进了强大的疾病建模.
科学领域:
- 计算生物学是一种计算生物学.
- 生物统计学 生物统计学
- 机器学习在医疗保健中的应用
背景情况:
- 训练预测模型通常需要大型,多样化的数据集,需要多个站点的数据聚合.
- 集中式数据聚合引发了隐私问题和后勤挑战.
- 联合学习 (FL) 为模型培训提供了一个分散的替代方案.
研究的目的:
- 开发和评估FL算法用于训练使用分布式数据集的时间到事件预测模型.
- 为了使个人水平的生存曲线预测,而不暴露敏感的患者数据.
主要方法:
- 提出了两种基于FL的方法来进行时间到事件的预测.
- 在Cox模型中使用了基线危险的内核光滑.
- 适用于右边审查数据的一般参数概率理论.
- 通过模拟和现实世界霍奇金淋巴瘤数据集验证的方法.
主要成果:
- 在四个模拟中,FL模型表现出与非分布式模型相比的性能.
- 与真实模型相比,在预测的生存概率中观察到微小的偏差.
- 现实世界的数据分析显示FL和霍奇金淋巴瘤的集中方法之间的性能相似.
结论:
- 拟议的FL方法有效地在分布式数据上训练时间到事件模型.
- 不共享个人级别数据和事件时间,保护患者的隐私.
- 实现了与集中式方法相当的预测性能.
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