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自动化成行为问题阿片类药物使用识别检查清单
Angus H Chatham1, Eli D Bradley1, Vanessa Troiani2
1Vanderbilt University School of Nursing, Nashville, Tennessee.
JAMA psychiatry
|April 9, 2025
概括
使用正则表达式的自动化方法在慢性疼痛患者中准确识别了有问题的阿片类药物使用,超过了传统的诊断代码. 这种技术有助于早期风险检测和未来对阿片类药物疼痛管理结果的研究.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 疼痛管理 疼痛管理
背景情况:
- 阿片类药物使用障碍 (OUD) 是阿片类药物管理的慢性疼痛患者的重大风险.
- 电子健康记录 (EHR) 提供了关于有问题的阿片类药物使用的大规模研究的潜力.
- 对OUD的诊断代码往往不可靠且未得到充分利用,因此需要改进识别方法.
研究的目的:
- 评估正则表达式的有效性,一种可解释的自然语言处理 (NLP) 技术,用于自动识别有问题的阿片类药物使用.
- 将这种自动化方法的性能与经过验证的临床工具 (成行为检查清单) 和传统诊断代码进行比较.
主要方法:
- 一项回顾性队列研究分析了2021-2023年间8063名慢性疼痛患者的非身份化EHR数据.
- 提取了自由文本的临床笔记,人口统计数据和诊断代码.
- 自动化方法与手动审查的持久集和100名患者的独立外部数据集进行了验证.
主要成果:
- 与诊断代码相比,自动化正则表达式方法表现出优越的性能.
- 在主要部位,F1得分为0.73与0.08,AUC为0.82与0.52.
- 在验证地点,F1得分为0.70与0.29,AUC为0.86与0.59.
结论:
- 使用正则表达式的自动数据提取可以有效地识别有问题的阿片类药物使用患者.
- 这种技术有助于更早地识别有风险的个体,并使新的研究途径成为可能.
- 它为研究慢性疼痛人群中阿片类药物使用的诊断代码提供了更可靠的替代方案.
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