使用语言模型对法语医学文本的否定线索和范围进行自动分析
Computers in biology and medicine
|August 23, 2025
概括
这项研究引入了一种基于BERT的方法,用于准确地识别法国医疗报告中的否定. 该模型实现了高性能,使其适用于现实世界的临床应用.
科学领域:
- 自然语言处理
- 医疗信息学
- 计算语言学
背景情况:
- 准确的医疗报告分析需要识别否定及其范围.
- 现有的工具通常需要对非英语语言 (如法语) 进行重大调整.
- 用语言模型进行的监督学习提供了一个有前途的方法来完成这个任务.
研究的目的:
- 开发和评估法国医疗报告中的自动否定提示和范围识别的监督学习方法.
- 适应并验证基于BERT的语言模型,考虑不同的临床注释和注释规则.
主要方法:
- 基于微调变压器的语言模型 (BERT) 在法国胸部CT扫描报告的私人数据库上用于否定检测.
- 将模型的适用性扩展到更广泛的临床注释和异质注释.
- 通过对模型的原始填充任务进行灾难性遗忘测试.
主要成果:
- 在胸部CT报告中达到99.4%的F1分数和94.5%的范围检测.
- 在更通用的生物医学报告中表现强,F1分数为98. 2% (提示值) 和90. 9% (范围).
- 用于否定识别的微调保留或改善了模型在最初的填充面具任务中的性能.
结论:
- 精心调整的BERT模型有效地检测法国医疗报告中的否定线索和范围.
- 该模型在各种注释规则和生物医学数据类型中表现出稳健性.
- 开发的方法非常适合在临床环境中实践实施.
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