解决事件报告延迟对生存分析的影响，提升短期风险评估精度。
Learning Survival Models with Right-Censored Reporting Delays
- 联合建模事件发生与报告过程的参数化风险函数
- 在行政删失条件下，风险评估准确率显著提升
- 适用于需要快速决策的医疗或公共安全场景
生存分析提供统计方法来建模事件发生的时间。当事件时间未在发生时被观测到，而是仅在报告后才披露时，便会产生报告延迟。这一问题在观测窗口因行政删失而较短时尤为关键，影响及时的风险评估。本文通过联合建模事件和报告过程的参数化风险函数，引入右删失的报告延迟。我们构造了模型参数的一致估计量，并开发了一种蒙特卡洛期望-最大化算法进行计算。为应对行政删失带来的挑战，我们提出一种迁移学习流程。实验表明，该方法在行政删失条件下显著提升了及时风险评估的准确性。
原文摘要 · Abstract (English)
Survival analysis provides statistical methods to model the time until an event occurs. Reporting delays arise when event times are not observed at their occurrence but are only revealed upon reporting. This issue is particularly critical for timely risk evaluation when the observation window is short due to administrative censoring. In this study, we incorporate right-censored reporting delays by jointly modeling parametric hazards for the event and reporting processes. We then construct a consistent estimator for the model parameters and develop a Monte Carlo expectation-maximization algorithm to compute it. To address the challenges posed by administrative censoring, we leverage these findings and propose a transfer-learning procedure. Experimental results demonstrate that our method improves the accuracy of timely risk evaluation under administrative censoring.
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