Models that can predict the occurrence of events ahead of time with low false-alarm rates are critical to the acceptance of decision support systems in the medical community. This challenging task is typically treated as a simple binary classification, ignoring temporal dependencies between samples, whereas we propose to exploit this structure. We first introduce a common theoretical framework unifying dynamic survival analysis and early event prediction. Following an analysis of objectives from both fields, we propose Temporal Label Smoothing (TLS), a simpler, yet best-performing method that preserves prediction monotonicity over time. By focusing the objective on areas with a stronger predictive signal, TLS improves performance over all baselines on two large-scale benchmark tasks. Gains are particularly notable along clinically relevant measures, such as event recall at low false-alarm rates. TLS reduces the number of missed events by up to a factor of two over previously used approaches in early event prediction.

翻译：能够提前预测事件发生且具有低误报率的模型，对于医疗领域决策支持系统的推广应用至关重要。这一具有挑战性的任务通常被简化为二分类问题，忽视了样本间的时序依赖关系，而本文提出利用这种结构特征。我们首先构建了一个统一动态生存分析与早期事件预测的通用理论框架。通过分析两个领域的优化目标，提出时序标签平滑（Temporal Label Smoothing, TLS）方法——这是一种更简单但性能最优的方法，能够保持预测结果随时间变化的单调性。通过将优化目标聚焦于预测信号更强的区域，TLS在两个大规模基准任务上的表现均优于所有基线方法。在临床相关指标（如低误报率下的事件召回率）上的提升尤为显著。与现有早期事件预测方法相比，TLS将漏报事件数量最多降低至原来的二分之一。