调整和评估深层假神经网络的生存数据与时间变化的共变量
Albert Whata1, Justine B Nasejje2, Najmeh Nakhaei Rad1,3
1Department of Statistics, University of Pretoria, Pretoria, South Africa.
Journal of applied statistics
|August 6, 2025
概括
深度假生存神经网络 (DSNN) 模型有效地预测了随时间变化的共变量生存概率. 这种深度学习方法显示了准确的生存分析的前景,与既有方法可比.
科学领域:
- 生物统计学 生物统计学
- 机器学习 机器学习
- 生存分析的分析.
背景情况:
- 传统的考克斯模型可能无法完全捕获具有时间变化的协变量的复杂生存数据.
- 深度学习,特别是深度假生存神经网络 (DSNN),在时间不变的生存数据方面表现出色.
- 将DSNN扩展到时间变化的共变量提供了改善生存功能估计的潜力.
研究的目的:
- 调整和评估深度假生存神经网络 (DSNN) 以在存在时间变化的共变量时预测生存概率.
- 将适应的DSNN与已建立的生存模型的性能进行比较.
- 为了验证DSNN在现实世界生存数据应用中的实用性.
主要方法:
- 深度假生存神经网络 (DSNN) 适应时间变化的共变量.
- 使用布里尔分数来评估特定时间点的预测准确性.
- 与模拟数据上的扩展考克斯,动态深度和多变量关节模型进行比较.
- 应用到一个现实世界的数据集与时间变化的共变量.
主要成果:
- 调整后的DSNN表现出强大的预测性能,Brier分数低于0.25对于显著的时间变化的共变量.
- 性能与模拟数据上的扩展考克斯,动态深度和多变量关节模型相美.
- 该模型的预测潜力在现实数据应用中得到了进一步的证实.
结论:
- 适应的深度假生存神经网络 (DSNN) 是一种可行且有效的工具,用于涉及时间变化的共变量的生存分析.
- DSNN为现有模型提供了有竞争力的替代方案,特别是在复杂的生存数据场景中.
- 这项研究强调了深度学习在提高生存预测准确度方面的潜力.
关键词:
62R07 它们是什么?92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B15 92B12 92B12 92B12 92B15 92B12 92B12 92B12 92B12 92B12 92B12 92B12 92B12 92B12 92B12 92B12 92B12 92B12 92B12 92B12 92B127 这是一个国家,这个国家,一个国家,一个国家,一个国家92B20 92B20 92B20 92B20 92B20 92B20 92B20 92B20 92B20 92B20 92B20 92B20 92B20 92B20 92B20 92B20 92B20深度神经网络是一种深度神经网络.动态-深入的命中.扩展的考克斯模型多变量联合模型多变量联合模型伪值是一种伪值.时间变化的共变量.
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