在受限传感条件下跨医院密集和MCI-like急诊分诊的早期恶化预测基准测试
Benchmarking Early Deterioration Prediction Across Hospital-Rich and MCI-Like Emergency Triage Under Constrained Sensing
AI总结：
本文提出了一种考虑数据泄漏的基准测试框架，评估在有限传感条件下早期恶化预测模型的性能，发现仅使用生命体征信息仍能保持较好的临床信号。
摘要：
急诊分诊决策是在严重信息限制下做出的，但大多数数据驱动的恶化模型评估使用在初始评估不可用的信号。我们提出了一种漏斗意识的基准测试框架，用于早期恶化预测，评估模型性能在现实、时间有限的传感条件下。使用从MIMIC-IV-ED衍生出的患者去重队列，我们比较了医院密集分诊与仅使用生命体征的MCI-like设置，限制输入为在出现第一小时内的信息。在多种建模方法中，当限制到生命体征时，预测性能仅略有下降，表明早期生理测量仍保留显著的临床信号。结构化消融和可解释性分析确定呼吸和氧合措施是早期风险分层的主要贡献者，模型在传感减少时表现出稳定、优雅的退化。这项工作提供了一个临床基础的基准，以支持在资源受限环境中评估和设计可部署的分诊决策支持系统。
英文摘要：
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.
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