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.

翻译：常规急诊护理中的分诊工具多为静态模式，未能充分利用临床医师实时观察到的简易行为线索。本研究开发了一种贝叶斯序贯更新框架，通过整合动态输入的生理线索生成经过校准且时间一致的风险评估。以前瞻性单中心队列研究数据为基础（日本院前急救患者队列，2025年2月至8月，n=2,221），我们以首次排尿时间作为床旁预测线索进行概念验证，评估其预测住院需求的效能。该模型在群体层面与累积入院曲线拟合度优异（积分平方误差0.002；均方根误差0.003；柯尔莫哥洛夫-斯米尔诺夫检验0.008；覆盖度0.98）。在个体层面，经年龄/性别校正后预测性能显著提升（受试者工作特征曲线下面积[t] 0.70 vs 未校正0.50），同时获得更低的Brier分数与正向校准斜率。Platt再校准在不改变区分度的前提下优化了概率标度，决策曲线分析显示在常规阈值下具有微小但正向的净收益。该框架可便捷扩展至多模态输入与外部验证，其设计初衷在于补充而非替代现有分诊体系。