使用机器学习预测患者离开未被看到的风险:在一个拥挤的急诊室进行了一项回顾性研究
Arianna Scala1, Teresa Angela Trunfio2, Massimo Majolo3
1Department of Public Health, University of Naples "Federico II", Naples, Italy.
BMC emergency medicine
|July 14, 2025
概括
紧急病房过度拥挤导致患者在没有被看到的情况下离开 (LWBS). 机器学习模型可以预测LWBS,等待时间和分类得分是关键因素,有助于医院资源规划.
科学领域:
- 医疗保健管理的管理
- 数据科学在医学中的数据科学
- 公共卫生 公共卫生
背景情况:
- 紧急部门 (ED) 过度拥挤是一个重要的医疗保健挑战.
- 增加的患者等待时间和无人看病 (LWBS) 率是过度拥挤的后果.
- 需要有效的策略来管理ED患者流量并减少LWBS.
研究的目的:
- 确定影响急诊室LWBS发生率的关键因素.
- 开发和评估机器学习 (ML) 模型来预测LWBS.
- 支持医院资源规划和改善患者流量管理.
主要方法:
- 马雷斯卡医院80614次ED访问 (2019-2023) 的回顾性分析.
- 对患者特征,操作变量和LWBS发生情况的统计分析.
- 评估了四种ML分类算法:随机森林,天真贝斯,决策树和物流回归.
主要成果:
- 随机森林模型实现了最高的性能,整体准确率为72%.
- 对于LWBS的关键预测因素包括等待时间,分类得分和访问方式.
- 该研究发现了操作变量和LWBS之间的显著相关性.
结论:
- 使用ML的预测建模可以有效地预测LWBS率.
- 识别等待时间和分类得分等关键预测因素对于干预至关重要.
- 这些发现可以为优化医院资源配置和患者流量的策略提供信息,最终减少LWBS.
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