机器学习来识别预测患者离开未被看到的儿科急诊室的属性
Julia Sarty1, Eleanor A Fitzpatrick2, Majid Taghavi3
1Department of Industrial Engineering, Dalhousie University, Halifax, NS, Canada.
CJEM
|July 28, 2023
概括
机器学习模型可以预测儿科患者在没有被看到的情况下离开急诊室 (ED). 关键因素包括ED患者负载,分拣时间和旅行时间,使得有针对性的干预措施.
科学领域:
- 紧急医疗 紧急医疗
- 医疗信息学 医疗信息学
- 机器学习应用 机器学习应用
背景情况:
- 没有被看到 (LWBS) 是儿科急诊室 (ED) 的一个重要问题.
- 预测建模可以帮助识别有风险的患者进行有针对性的干预.
- 了解LWBS驱动器对于改善患者流量和护理质量至关重要.
研究的目的:
- 描述从加拿大儿科ED离开没有被看的患者 (LWBS).
- 开发机器学习模型,根据患者的关键属性来预测LWBS.
- 确定在这个人群中与LWBS相关的最有影响力的因素.
主要方法:
- 来自加拿大儿科医院的行政ED数据的分析 (2017年4月至2020年3月).
- 利用监督机器学习二进制分类算法 (包括XGBoost) 来预测LWBS.
- 用于数据集平衡和用于超参数调整的网格搜索的合成少数群体过量采样技术 (SMOTE).
主要成果:
- 该研究分析了101,266次ED访问,其中5.7%的结果是LWBS.
- 性能最高的XGBoost模型在预测LWBS时实现了95%的回忆率和87%的灵敏度.
- 最有影响力的预测因素包括ED患者负载,分组时间,驾驶距离,停留时间和患者年龄.
结论:
- 机器学习模型有效地使用加拿大儿科ED的行政数据预测LWBS.
- 确定了五个关键属性,这些属性显著影响LWBS预测.
- 这些发现支持开发个体患者级干预措施,以减轻LWBS和提高ED效率.
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