跨机构的自然语言处理可靠的临床关联研究:一个方法的探索探索
Madhumita Sushil1, Atul J Butte1, Ewoud Schuit2
1Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, USA.
Journal of clinical epidemiology
|January 14, 2024
概括
临床笔记的自然语言处理 (NLP) 可以提取患者数据用于健康研究. 然而,从NLP提取的暴露中得出的关联需要谨慎解释,因为与手工提取的显著差异.
科学领域:
- 临床信息学是一种临床信息学.
- 生物统计学 生物统计学
- 医疗服务研究 医疗服务研究
背景情况:
- 电子病历 (EMR) 包含有价值的患者数据.
- 自然语言处理 (NLP) 便于提取这些数据用于研究.
- 目前临床研究中的NLP方法需要进一步研究.
研究的目的:
- 调查NLP模型在抽取暴露结果关联研究的研究变量时的可靠性.
- 为了比较从NLP提取的暴露与手动提取的暴露中得出的关联估计.
主要方法:
- 使用来自ICU的方便样本进行了关联研究.
- 比较NLP提取与手动提取的暴露 (就业,生活状况,物质使用).
- 多种NLP模型,培训范式,结果测量和统计方法.
主要成果:
- 在NLP提取和手工提取的暴露之间观察到的关联估计的实质差异.
- 关联错误与NLP模型性能 (F1分数) 的相关性较弱.
- 研究包括1,174名参与者;NLP模型训练了多达2,528份外部报告.
结论:
- 从NLP提取的暴露中获得的关联应谨慎解释.
- 需要进行进一步的研究,以确定在医学关联研究中可靠使用NLP的条件.
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