医学文本处理中的自然语言处理:一个范围的文献综述
Luis B Elvas1, Ana Almeida2, João C Ferreira3
1Department of Logistics, Molde University College, Molde 6410, Norway; Inov Inesc Inovação - Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal; Breast Cancer Research Program, Champalimaud Foundation, Lisbon, Portugal; ISTAR, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal.
International journal of medical informatics
|July 24, 2025
概括
现代自然语言处理 (NLP) 技术,特别是基于BERT的模型,擅长从复杂的医学文本中提取信息. 这些先进的方法对改善跨语言医学数据解释有很大的前景.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 数字化医疗数据正在呈指数级增长,医疗保健专业人员的复杂性增加.
- 自然语言处理 (NLP),特别是命名实体识别 (NER),对于从临床文本中提取信息至关重要.
- 像BERT这样的基于变压器的模型已经彻底改变了医疗数据的解释.
研究的目的:
- 审查和分析最近的NLP医疗文本处理方法.
- 检查技术,绩效指标和NLP在医疗保健方面的进展.
- 评估跨不同语言和医疗保健环境的NLP应用.
主要方法:
- 在Scopus和PubMed (2019-2024) 中使用PRISMA方法的范围文献搜索.
- 包括专注于语言模型微调和医疗保健中的信息提取的研究.
- 一个特定的搜索查询针对相关的NLP技术.
主要成果:
- 包括31项研究,其中基于BERT的方法,神经网络和CRF/LSTM技术占主导地位.
- 这些方法始终实现了F1得分超过85%.
- 研究涵盖了多种语言,主要是英语和中文,并解决了数据隐私和有限的数据.
结论:
- 现代NLP,特别是BERT和混合方法,对医疗文本处理有很大的前景.
- 跨语言适应和数据可用性的挑战仍然存在.
- 这些技术有可能提高医疗数据的解释和分析.
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