追踪持续的术后阿片类药物使用:一项概念验证研究,展示了自然语言处理的用例
Eri C Seng1, Soraya Mehdipour1, Sierra Simpson1
1Division of Perioperative Informatics, Department of Anesthesiology, University of California San Diego, La Jolla, California, USA.
Regional anesthesia and pain medicine
|July 7, 2023
概括
自然语言处理 (NLP) 可以通过分析临床笔记,准确地识别大脊椎手术患者的持续性术后阿片类药物使用情况. 这项技术为管理阿片类药物危机和改善患者护理提供了一个有希望的工具.
科学领域:
- 医疗信息学 医疗信息学
- 临床数据分析 临床数据分析
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 和自然语言处理 (NLP) 在医疗保健中越来越重要.
- 确定持续的术后阿片类药物使用对于患者护理和解决阿片类药物危机至关重要.
- 非结构化临床文本包含有价值的数据,以了解阿片类药物使用模式.
研究的目的:
- 为了证明NLP引擎能够识别持续的术后阿片类药物使用的能力.
- 评估NLP在分析临床笔记中对脊椎重大手术后阿片类药物使用状况的准确性.
主要方法:
- 从接受大脊椎手术的患者中提取了临床文件 (2015年7月至2021年8月).
- 术后持续的阿片类药物使用定义为>=手术后3个月的阿片类药物使用.
- 将NLP引擎应用于门诊随访笔记,并将结果与手册临床医生审查进行比较.
主要成果:
- 该研究包括965名患者; 73.1%的人表现出持续的阿片类药物使用.
- 在确定阿片类药物使用状态时,NLP引擎实现了92.9%的整体准确性.
- 在95.6%的案例中,NLP引擎正确识别了持续使用阿片类药物,在86.1%的案例中没有持续使用.
结论:
- NLP是一种可行的工具,用于提取和分析非结构化的临床数据,以识别持续的术后阿片类药物使用.
- 利用NLP可以提供对阿片类药物危机的见解,并提高患者层面的护理.
- 需要进一步的研究来将NLP纳入医疗保健机构内的临床决策支持系统.
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