预测在急诊室被遗忘的情况作为一个动态的风险预测
Yaniv Ravid1, Rouba Ibrahim2, Junqi Hu3
1University of Toronto, Rotman School of Management, Canada.
The American journal of emergency medicine
|September 9, 2025
概括
动态更新机器学习 (ML) 模型可以显著改善预测可能离开急诊室 (ED) 没有被看到 (LWBS) 的患者. 与静态模型相比,这种方法可以识别更多的LWBS患者,减少50%的遗漏病例.
科学领域:
- 医疗保健分析 医疗保健分析
- 机器学习在医学中的应用
- 紧急情况部门运营部 运营部
背景情况:
- 患者离开急诊室 (ED) 而没有被看病 (LWBS) 的高比例会对患者流动和护理产生负面影响.
- 准确预测LWBS风险对于实施有针对性的干预措施和提高ED效率至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测ED患者的LWBS风险.
- 为了比较动态ML模型的准确性,该模型随着时间的推移更新预测,与静态模型进行比较.
主要方法:
- 使用18个月的ED访问数据开发了两个XGBoost分类模型:一个静态模型和一个动态模型,在30分钟后更新预测.
- 这些模型在一个大型学术医疗校园的六个月后续数据上进行了测试.
- 这项研究分析了150,959名ED患者的队列.
主要成果:
- 动态ML模型实现了0.86的AUROC,超过了静态模型的AUROC0.80.
- 与静态模型相比,动态模型减少了大约50%的LWBS遗漏病例.
- 动态模型没有观察到错误阳性增加.
结论:
- 动态更新LWBS风险预测超过患者的ED等待时间显著提高预测准确性.
- 这种方法在识别患有LWBS高风险的患者方面比传统的静态模型更有效.
- 实施动态ML模型可以大幅减少遗漏的LWBS病例,并改善ED患者的治疗.
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