标题： 基于贝叶斯序贯建模的排尿时间动态ED分诊 显示英文标题

标题： Bayesian Sequential Modeling of Time-to-Urination for Dynamic ED Triage

摘要： 分诊工具在常规急诊护理中大多是静态的，未能利用临床医生实时注意到的简单行为线索。 在这里，我们开发了一个贝叶斯、依次更新的框架，该框架整合了进入的线索以产生校准的、时间一致的风险。 使用日本一个中心的救护车到达前瞻性队列（2025年2月至8月；n=2,221），我们评估了首次排尿时间（TTU）作为预测住院的初步床边线索。 人群层面与累积入院曲线的拟合非常好（积分平方误差 0.002；RMSE 0.003；Kolmogorov-Smirnov 0.008；覆盖率 0.98）。 在患者层面，经过年龄/性别调整后性能显著提高（AUC[t] 0.70 对比未调整的 0.50），Brier 分数较低且正校准斜率。 Platt 校准改进了概率缩放而不改变区分度，决策曲线分析显示在常见阈值下有小而有利的净收益。 此框架易于扩展到多模式输入和外部验证，并设计为补充而非替代现有的分诊系统。 显示英文摘要

摘要： Triage tools in routine emergency care are largely static, failing to exploit simple behavioral cues clinicians notice in real time. Here, we developed a Bayesian, sequentially updating framework that integrates incoming cues to produce calibrated, time-consistent risk. Using a prospective single-center cohort of ambulance arrivals in Japan (February-August 2025; n=2,221), we evaluated time to first urination (TTU) as a proof-of-concept bedside cue for predicting hospital admission. Population-level fit to the cumulative admission curve was excellent (integrated squared error 0.002; RMSE 0.003; Kolmogorov-Smirnov 0.008; coverage 0.98). At the patient level, performance improved markedly with age/sex adjustment (AUC[t] 0.70 vs. 0.50 unadjusted), with lower Brier scores and positive calibration slopes. Platt recalibration refined probability scaling without altering discrimination, and decision-curve analysis showed small, favorable net benefit at common thresholds. This framework is readily extensible to multimodal inputs and external validation and is designed to complement, not replace, existing triage systems.