对比法国临床文本的NER方法,与易于重复使用的管道
Thibault Hubert1,2, Ghislain Vaillant1,2, Olivier Birot1,2
1Inria, HeKA, PariSanté Campus, Paris, France.
Studies in health technology and informatics
|August 23, 2024
概括
我们对来自电子健康记录 (EHR) 的法语临床文本评估了四种命名实体识别 (NER) 方法. 语言模型的表现优于词典方法,这表明预先训练的模型可能足以执行临床NER任务.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 电子健康记录 (EHR) 中的临床文本对观察性研究有丰富的信息.
- 跨机构和语言的临床文本的异质性给命名实体识别 (NER) 工具的性能带来了挑战.
- 由于分享敏感临床数据的困难,标准化基准测试至关重要.
研究的目的:
- 为了对不同法国临床机构的四种命名实体认可 (NER) 方法进行比较.
- 提供一个开源的,可重复使用的管道,用于评估医学领域的NLP工具.
- 用临床数据评估微调对现有NER模型的影响.
主要方法:
- 四种NER方法的比较:三个基于语言模型,一个基于字典.
- 用了三个不同的法国临床机构进行评估.
- 使用medkit Python库开发和共享一个开放的,可重复使用的基准测试管道.
- 包括使用单个或多个公司的微调策略.
主要成果:
- 与基于字典的方法相比,语言模型显示出更高的性能.
- 对特定体的微调并不总是比一般生物医学模型产生显著的改进.
- 该研究强调了现代NLP模型对临床文本分析的有效性.
结论:
- 在法语临床文本中,语言模型对NER非常有效.
- 在特定的临床体上对预训练的生物医学NLP模型进行广泛微调的必要性受到质疑.
- 分享开放和可适应的基准测试管道对于推进临床NLP研究至关重要.
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