混合自然语言处理工具用于西班牙语医学文本的语义注释
Leonardo Campillos-Llanos1, Ana Valverde-Mateos2, Adrián Capllonch-Carrión3
1ILLA - CSIC (Spanish National Research Council), C/Albasanz 26-28, 28037, Madrid, Spain. leonardo.campillos@csic.es.
BMC bioinformatics
|January 8, 2025
概括
这项研究介绍了MedSpaNER,这是西班牙医疗命名实体识别 (NER) 和属性提取的新工具. MedSpaNER在临床文本中识别医疗概念和属性方面取得了高准确性,改善了从西班牙医疗文件中提取信息.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
背景情况:
- NLP从非结构化的临床文本中提取信息,如病例报告和试验标准.
- 准确的信息提取支持队列识别和临床记录分析.
- 为西班牙医疗NER引入了一个新的深度学习和基于词典的工具.
研究的目的:
- 开发和验证西班牙医疗文本的命名实体识别 (NER) 工具.
- 执行医疗NER,正常化,药物提取和时间/体验者属性检测.
- 提供一个混合工具,将基于变压器的模型与词典和规则集成在一起.
主要方法:
- 使用深度学习 (基于变压器的模型) 和基于词典的方法开发了一种混合NER工具.
- 调整了NegEx和HeidelTime规则,为西班牙医学文本创建了一个专门的词典.
- 标注了1200个文本的语料库,实现了高度的标注者间协议 (实体F1=0.841,属性F1=0.881).
主要成果:
- 内部验证显示F1模型的得分高达0.915.
- 在100项临床试验的外部验证中,平均F1得分为0.858.8.
- 在100个匿名临床病例的外部验证中,平均F1得分为0.910.
结论:
- 开发的工具MedSpaNER在西班牙医疗NLP任务中表现出很高的性能.
- 该工具,其代码和注释的语料库是公开发布的,以促进研究.
- 在https://claramed.csic.es/medspaner上提供MedSpaNER,并在https://github.com/lcampillos/medspaner.com上提供代码.
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