翻译对生物医学信息提取的影响:实践临床笔记的实验
Christel Gérardin1, Yuhan Xiong1,2, Perceval Wajsbürt3
1Institut Pierre Louis d'Epidémiologie et de Santé Publique, Sorbonne Université, Institut National de la Santé et de la Recherche Médicale, Paris, France.
JMIR medical informatics
|April 10, 2024
概括
法国自然语言处理模型在法语医学概念提取方面表现优于英语模型. 与翻译的英语方法相比,即使使用有限的注释数据,本地法语模型也更有效.
科学领域:
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 生物医学自然语言处理 (NLP) 通常依赖于英语模型,尽管在创建注释数据集方面存在挑战.
- 机器翻译的进步为使用英语NLP工具与其他语言提供了潜力.
研究的目的:
- 评估英语NLP工具是否使用翻译的法语医学概念,与在法语临床笔记上训练的法语NLP模型的性能相匹配.
- 为了比较本地法语NLP模型与英语NLP模型的有效性,为法语医学概念提取和规范化提供翻译.
主要方法:
- 通过使用母语法语模型和英语模型与翻译进行了比较研究.
- 法国本土的方法涉及单独的命名实体识别 (NER) 和规范化步骤.
- 英语语言的方法涉及翻译,然后采用两步过程或以术语为导向的同时提取和规范化方法. 评估使用法语,英语和双语注释数据集.
主要成果:
- 法国本土的NLP方法获得了0.51 (95%CI0.47-0.55) 的F1总得分,这一得分显著更高.
- 翻译成英语的方法给出了较低的F1分数,分别为0.39 (95% CI 0.34-0.44) 和0.38 (95% CI 0.36-0.40).
- 绩效评估涵盖了NER,标准化和翻译阶段.
结论:
- 与翻译的英语方法相比,本地法语NLP模型在提取和规范法语医疗概念方面表现优异.
- 即使有有限的注释法语数据,本地法语模型在处理法语医学文本方面也更有效.
- 翻译模型的改进并不能完全弥合原生和翻译方法之间观察到的性能差距.
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