在COVID-19期间识别阿片类药物复发,使用自然语言处理全国退伍军人卫生管理局电子医疗记录数据
Nicholas A Livingston1, Amar D Mandavia1, Anne N Banducci2
1National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System.
Journal of psychopathology and clinical science
|March 20, 2025
概括
自动化自然语言处理 (NLP) 工具可以在电子健康记录中检测阿片类药物复发. 在COVID-19大流行开始后,阿片类药物复发风险增加,这表明需要加强监测.
科学领域:
- 数字健康数字健康
- 临床信息学是一种临床信息学.
- 公共卫生监督是对公共卫生的监督.
背景情况:
- 阿片类药物使用障碍 (OUD) 需要持续监测复发风险.
- 电子病历 (EMR) 包含有价值的非结构化数据,用于临床见解.
- 需要自动化方法来有效地分析EMR以寻找物质使用障碍标志物.
研究的目的:
- 开发和验证一种自然语言处理 (NLP) 工具,用于自动检测EMR产生的阿片类药物复发.
- 分析COVID-19大流行开始前后OUD的退伍军人中阿片类药物复发的趋势.
主要方法:
- 利用NLP从老兵EMR进度笔记中提取阿片类药物复发事件和时间.
- 在流行病暴露队列 (n=53,803) 和匹配的流行病前队列 (n=53,803) 之间比较复发趋势.
- 评估了NLP工具的性能,75%的回忆率和94%的精度.
主要成果:
- 该NLP工具在识别阿片类药物复发时表现出中度敏感性和高特异性.
- 与流行病前趋势相比,在流行病后发病队列中,阿片类药物复发的几率相对较高.
- 尽管流行后的心理健康遭遇较少,但发现复发风险较高.
结论:
- 在阿片类药物使用障碍中,NLP为自动监控和风险预防提供了一个有希望的方法.
- 该研究强调,在COVID-19大流行期间,退伍军人中阿片类药物复发风险增加.
- 这些发现支持NLP在OUD管理的临床结果研究中的应用.
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