从临床笔记中有效提取药物信息:以2种语言进行评估
Thibaut Fabacher1,2,3, Erik-André Sauleau1,2, Emmanuelle Arcay1
1Service de Santé Publique, University Hospital of Strasbourg, Strasbourg, 67000, France.
Journal of the American Medical Informatics Association : JAMIA
|October 10, 2025
概括
一种新的基于变压器的自然语言处理 (NLP) 方法有效地从临床文本中提取药物信息. 这种方法为法语和英语文档提供了高精度和较低的计算成本.
科学领域:
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
- 生物医学文本挖掘 生物医学文本挖掘
背景情况:
- 从临床叙述中提取药物信息对于患者安全和药物监督至关重要.
- 现有的自然语言处理 (NLP) 方法在准确性,计算成本或跨语言可移植性方面可能存在局限性.
研究的目的:
- 评估一种新的基于变压器的NLP架构,用于从临床文本中提取药物细节.
- 评估拟议方法的准确性,计算效率和跨语言可移植性.
主要方法:
- 开发和训练了一个基于变压器的架构,用于命名实体识别和药物信息的关系提取.
- 从2018 n2c2共享任务中对法国临床体和英国临床体进行了评估.
- 将性能和计算成本与现有的基于变压器的方法进行比较.
主要成果:
- 拟议的架构实现了竞争关系提取性能 (法语的F-措施为0.82,英语为0.96).
- 端到端提取 (NER和RE) 产生了F1得分,分别为0.69 (法语) 和0.82 (英语).
- 与现有方法相比,显著降低了10%的计算成本.
结论:
- 新型NLP架构在从法语和英语临床文本中提取药物信息方面表现出高性能.
- 该方法提供了良好的准确性和减少计算影响的平衡,适合医院IT环境.
- 这种方法提高了在不同临床环境中提取药物信息的可移植性和效率.
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