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Updated: Sep 17, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
横向联合学习和考克斯模型的评估
Frank Westers1, Sam Leder1, Lucia Tealdi2
1Applied Cryptography & Quantum Applications, Netherlands Institute for Applied Scientific Research (TNO), The Hague, Netherlands.
联合学习可以在各机构之间协作训练Cox比例危险模型,而无需共享原始患者数据. 这种方法可以提高生存分析的准确性,同时保持数据隐私.
科学领域:
- 医学研究 医学研究
- 计算机科学 计算机科学
- 统计 统计 统计 统计
背景情况:
- 考克斯的比例危险模型对于医学生存分析至关重要.
- 训练准确的模型需要大量的数据集,通常在各个机构之间分散.
- 隐私问题阻碍了合作模式培训的直接数据共享.
研究的目的:
- 开发保护隐私的算法,用于使用联合学习训练Cox模型.
- 在一个联合的环境中,为Schoenfeld余数引入安全的计算方法.
- 为了证明联合考克斯回归的准确性和好处.
主要方法:
- 开发了用于考克斯模型培训的联合学习算法.
- 利用生存堆叠来实现分布式学习.
- 引入了用于Schoenfeld余量的安全计算.
主要成果:
- 经验结果证明了联合考克斯回归方法的准确性.
- 该方法有效地解决了数据碎片化和隐私限制的问题.
- 开源实现促进了更广泛的采用.
结论:
- 联合学习为保护隐私的生存分析提供了可行的解决方案.
- 开发的算法和安全诊断增强了Cox模型在分布式环境中的实用性.
- 这项工作促进了协作医学研究,同时维护了患者的保密性.
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