在有限生命体征下评估早期恶化预测，发现仅用前一小时数据仍可有效预警。
Benchmarking Early Deterioration Prediction Across Hospital-Rich and MCI-Like Emergency Triage Under Constrained Sensing
- 构建泄露感知框架，模拟真实急诊初诊时的受限感知条件
- 仅用生命体征时预测性能下降温和，呼吸与氧合指标最关键
- 适合开发资源受限场景下的临床决策支持系统
急诊分诊决策常面临严重信息不足，但多数数据驱动的恶化预测模型却基于初始评估中无法获取的信号进行评估。本文提出一种泄漏感知的基准评估框架，用于在现实、时间受限的感知条件下评估早期恶化预测性能。基于MIMIC-IV-ED去重患者队列，我们比较了医院丰富分诊与仅限生命体征的MCI-like设置，将输入限制在患者就诊后一小时内可获得的信息。在多种建模方法中，当仅使用生命体征时，预测性能仅小幅下降，表明早期生理测量仍蕴含丰富临床信号。结构化消融与可解释性分析识别出呼吸和氧合指标是早期风险分层中最关键的贡献因素，且模型在传感减少时表现出稳定、渐进式退化。本研究为资源受限环境下可部署的分诊辅助系统的评估与设计提供了临床基础基准。
原文摘要 · Abstract (English)
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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