使用自然语言处理和机器学习识别高风险的阿片类药物过量治疗急诊室患者
Amanda Sharp1, Gareth J Parry2, Gabriel Ríos Pérez1
1Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America.
Journal of substance use and addiction treatment
|May 5, 2025
概括
机器学习模型准确地预测了使用电子健康记录的急诊室患者的致命阿片类药物过量服用风险. 这些工具可以识别高风险个体,以便及时进行干预,并改善对阿片类药物使用障碍的护理.
科学领域:
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
- 临床信息学 临床信息学
背景情况:
- 紧急服务部门 (ED) 对于识别处于阿片类药物过量风险较高的人来说至关重要.
- 这项研究旨在开发机器学习 (ML) 模型,以预测ED访问后12个月内致命的阿片类药物过量服用.
研究的目的:
- 预测阿片类药物过量死亡风险在紧急病房访问后的患者.
- 利用电子健康记录 (EHR),包括临床笔记,用于预测建模.
主要方法:
- 将EHR数据与阿片类药物过量死亡记录 (2011-2019) 合并.
- 利用相互信息进行特征选择,将1336个特征减少到50个.
- 在70%和30%的样本上训练并验证了XGBoost,随机森林和回归模型.
主要成果:
- 功能选择从EHR临床笔记中确定了37个重要的预测因素.
- 模型在预测阿片类药物过量死亡方面实现了高准确性 (92%),精度 (75%) 和回忆 (57%).
- 所有模型都表现出满意的校准,概率值>0.5.
结论:
- 使用结构化和非结构化EHR数据的ML算法可以有效地识别患有致命阿片类药物过量服用风险的患者.
- 这些预测工具可以指导风险患者的干预,改善临床决策.
- 开发的模型提高了ED发起的阿片类药物使用障碍服务的及时性和有效性.
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