提出可独立优化的评分规则，提升竞态风险生存分析的准确性和速度。
Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks
- 设计可分离的严格正确评分规则，支持数据子集独立优化
- 在4个真实数据集上超越12种主流模型，时间预测更准且计算更快
- 适合需要高精度、跨时间预测和快速训练的医疗与工业场景
右删失数据中部分结果因观察期有限而缺失，生存分析（时间到事件分析）关注事件发生的时间预测。当存在多个可能结局时，需解决竞态风险问题——即预测最可能发生事件，这一方向研究较少。传统竞态风险模型将架构与损失函数耦合，限制了可扩展性。本文提出一种严格正确的删失调整可分离评分规则，使每个样本可独立评估，从而支持基于梯度的随机优化。该方法用于高效梯度提升树模型SurvivalBoost，其在4个真实数据集上，无论是竞态风险还是常规生存分析任务中，均优于12种先进模型，兼具良好校准性、任意时间窗预测能力及比现有方法更快的计算速度。
原文摘要 · Abstract (English)
When dealing with right-censored data, where some outcomes are missing due to a limited observation period, survival analysis -- known as time-to-event analysis -- focuses on predicting the time until an event of interest occurs. Multiple classes of outcomes lead to a classification variant: predicting the most likely event, a less explored area known as competing risks. Classic competing risks models couple architecture and loss, limiting scalability.To address these issues, we design a strictly proper censoring-adjusted separable scoring rule, allowing optimization on a subset of the data as each observation is evaluated independently. The loss estimates outcome probabilities and enables stochastic optimization for competing risks, which we use for efficient gradient boosting trees. SurvivalBoost not only outperforms 12 state-of-the-art models across several metrics on 4 real-life datasets, both in competing risks and survival settings, but also provides great calibration, the ability to predict across any time horizon, and computation times faster than existing methods.
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