通过可解释的时间到事件机器学习方法探索医院拥挤情况
Tobias Haraldsson1, Luca Marzano1, Harsha Krishna1
1KTH Royal Insitute of Technology, Stockholm, Sweden.
Studies in health technology and informatics
|August 23, 2024
概括
应急部门 (ED) 过度拥挤分析使用了一种新的时间到事件方法. 现实世界的数据揭示了像扫描和分拣等关键因素影响患者逗留时间的长度,为政策提供了见解.
科学领域:
- 医疗保健 运营 研究 研究 研究
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
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.
- 了解ED的操作动态对于改善患者流量和资源配置至关重要.
研究的目的:
- 为医疗保健生产数据引入一种新的时间到事件分析框架.
- 确定影响患者在ED停留时间 (LOS) 的关键因素.
主要方法:
- 应用时间到事件分析,使用传统的生存估计和机器学习模型.
- 使用Shapley添加式解释 (SHAP) 来实现模型的解释性.
- 分析了现实世界ED生产数据.
主要成果:
- 确定ED LOS的关键预测因素:扫描,紧急访问状态,患者年龄,分组级别和医疗报警单位类别.
- 证明了生存分析和SHAP值在发现可操作见解方面的实用性.
- 突出了急救部门与其他医院部门的相互联系.
结论:
- 时间到事件的方法为分析ED过度拥挤提供了有价值的方法.
- 数据驱动的洞察力可以为政策设计和运营改进提供信息,以减轻ED过度拥挤.
- 对已识别的特征进行进一步的调查可以提高对ED患者流量的理解和管理.
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