机器学习模型检测阿片类药物滥用在急诊室的患者在分拣
medRxiv : the preprint server for health sciences
|August 12, 2025
概括
机器学习模型可以在急诊室分拣期间识别阿片类药物滥用患者,为手动查提供可行的早期干预替代方案. 这种方法利用例行收集的数据在医生评估之前标记有风险的个体.
科学领域:
- 紧急医疗 紧急医疗
- 数据科学是数据科学.
- 公共卫生 公共卫生
背景情况:
- 紧急部门 (ED) 遭遇是错过了启动阿片类药物滥用治疗的机会.
- 手动查阿片类药物滥用是资源密集且不常见的.
- 机器学习 (ML) 提供了一个潜在的解决方案,用于识别患者在分拣.
研究的目的:
- 确定使用ML用于在ED分拣中识别阿片类药物滥用患者的可行性.
- 用例行收集的分拣数据来评估ML模型的性能.
主要方法:
- 1,123个ED遭遇的回顾性队列研究 (2020年9月 - 2023年3月).
- 候选分组特征包括人口统计,敏度,到达时间,主要投诉,并发症和药物.
- 模型使用F1分数,AUPRC,精度,回忆和AUROC进行评估; SHAP用于解释性.
主要成果:
- ML模型的性能与阿片类药物相关的诊断代码相提并论.
- 随机森林 (F1=0.75) 和梯度提升 (F1=0.77) 显示出高性能.
- 最重要的预测因素包括先前的药物使用诊断代码,分类敏度和年龄.
结论:
- 使用分拣数据的ML分类器可用于标记阿片类药物滥用.
- 这种方法可以实现早期减少危害的干预.
- 建议在多个地点的研究中进行进一步的验证和偏差评估.
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