在法语中翻译UMLS概念以改善医疗实体链接:基于SapBERT的方法
Amaury Fierens1, Alexandre Englebert1,2, Sébastien Jodogne1
1ICTEAM, Louvain School of Engineering, UCLouvain, Belgium.
Studies in health technology and informatics
|May 17, 2025
概括
这项研究通过培训SapBERT对翻译的医学术语来增强法国医疗实体联系 (MEL). 与现有的跨语言方法相比,这种方法提高了性能,解决了法语医学概念提取的数据稀缺问题.
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:
- 医疗实体链接 (MEL) 对于分析非结构化临床数据至关重要.
- SapBERT是一个有效的英语MEL工具,但由于培训数据有限,它的法语版本缺乏性能.
- 现有的跨语言方法与法语医疗概念识别作斗争.
研究的目的:
- 调查SapBERT培训的机器翻译法语医学术语.
- 为了提高法国医疗实体链接的准确性.
- 为了克服SapBERT的法国医疗培训数据不足的挑战.
主要方法:
- 使用UMLS Metathesaurus的机器生成的法语翻译.
- 使用这些翻译的法语医学术语训练了SapBERT模型.
- 在QuaeroFrenchMed基准数据集上评估了模型的性能.
主要成果:
- 拟议的培训策略显著超过了标准的跨语言SapBERT.
- 在法语的SNOMED-CT医疗实体链接中取得了卓越的表现.
- 证明了对低资源语言使用翻译术语的有效性.
结论:
- 培训 SapBERT 关于翻译的 UMLS Metathesaurus 是一个可行的策略来增强法语 MEL.
- 这种方法有效地解决了法国医疗概念编码的数据稀缺问题.
- 这些发现为改善跨语言医学NLP应用提供了实际解决方案.
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