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Updated: Jan 15, 2026

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通过标志性梯度增强方法进行动态生存分析,并将其应用于移植数据

Niloofar Shabani1, Mehdi Yaseri1, Rasoul Alimi2

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

BMC medical informatics and decision making
|October 10, 2025
PubMed
概括

标志性梯度提升模型 (LGBM) 为复杂的关系提供了卓越的动态生存预测,在具有高审查率和以后的里程碑时间的大数据集中表现优于传统方法.

关键词:
动态风险预测 动态风险预测渐变增强了树木的增长.纵向标记器 纵向标记器 纵向标记器机器学习在生存中的作用对生存分析的分析.

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