使用数据驱动的机器学习预测紧急严重性指数 (ESI) 水平,住院和急诊室的入院病房
Steve Agius1, Vincent Cassar2, Caroline Magri2
1University of Malta, Msida, Malta. stephen.agius@um.edu.mt.
BMC medical informatics and decision making
|July 29, 2025
概括
机器学习模型准确地预测急诊室 (ED) 患者优先级,入院概率和入院病房. 结合临床数据显著提高了预测准确度,提高了紧急护理效率.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 紧急医疗 紧急医疗
背景情况:
- 紧急服务部门 (ED) 需要有效的分拣,以确保患者安全和资源管理.
- 准确的患者优先安排对于及时提供护理至关重要.
- 来自马耳他Mater Dei医院的大型数据集,涵盖六年和653,546次访问,被用于分析.
研究的目的:
- 开发和评估机器学习模型,以预测关键的急诊室结果.
- 评估结合临床数据对预测模型性能的影响.
- 为ED分类中数据驱动的决策提供一个框架.
主要方法:
- 开发了两个极端梯度增强 (XGBoost) 模型.
- 第一阶段的模型使用了人口和症状数据;第二阶段纳入了关键血液检测结果.
- 数据预处理包括处理缺失值,类平衡 (SMOTE) 和特征编码. 模型评估使用了AUC-ROC和校准曲线.
主要成果:
- 这两种模型都表现出了显著的预测能力.
- 结合临床数据的第二阶段模型显示出更好的准确性:患者优先级 (0.76),入院预测 (0.82),和入院病房预测 (0.86).
- 改进证实了临床数据集成对优化预测的价值.
结论:
- 机器学习模型有效地预测了关键的ED结果.
- 通过XGBoost整合人口和临床数据,为ED分拣提供了一个强大的框架.
- 这些数据驱动的洞察力有可能改善患者流动,减少等待时间,并优化紧急护理中的资源配置.
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