Emergency triage decisions are made under severe information constraints, yet most data-driven deterioration models are evaluated using signals unavailable during initial assessment. We present a leakage-aware benchmarking framework for early deterioration prediction that evaluates model performance under realistic, time-limited sensing conditions. Using a patient-deduplicated cohort derived from MIMIC-IV-ED, we compare hospital-rich triage with a vitals-only, MCI-like setting, restricting inputs to information available within the first hour of presentation. Across multiple modeling approaches, predictive performance declines only modestly when limited to vitals, indicating that early physiological measurements retain substantial clinical signal. Structured ablation and interpretability analyses identify respiratory and oxygenation measures as the most influential contributors to early risk stratification, with models exhibiting stable, graceful degradation as sensing is reduced. This work provides a clinically grounded benchmark to support the evaluation and design of deployable triage decision-support systems in resource-constrained settings.

翻译：急诊分诊决策在严重信息约束下进行，然而大多数数据驱动的恶化模型在评估时使用了初始评估阶段无法获取的信号。我们提出了一种面向早期恶化预测的泄露感知基准测试框架，该框架在现实且时间受限的感知条件下评估模型性能。利用从MIMIC-IV-ED导出的患者去重队列，我们将医院丰富分诊与仅基于生命体征的轻度认知障碍类设置进行了比较，并将输入限制在就诊首小时内可获取的信息范围内。在多种建模方法中，当仅依赖生命体征时，预测性能仅出现适度下降，表明早期生理测量仍保留了显著的临床信号。结构化消融与可解释性分析识别出呼吸与氧合指标是早期风险分层中最具影响力的贡献因素，且模型在感知缩减时表现出稳定而优雅的性能退化。本工作为在资源受限场景下评估与设计可部署的分诊决策支持系统提供了临床依据的基准。