医疗保健语言模型及其微调信息提取:范围审查
Miguel Nunes1, Joao Bone2, Joao C Ferreira1,3,4
1ISTAR, Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal.
JMIR medical informatics
|October 21, 2024
概括
域调整增强了用于医疗保健信息提取的变压器模型. 欧洲葡萄牙语需要在医疗保健语言模型中发展,从英语和中文的进步中汲取教训.
科学领域:
- 自然语言处理自然语言处理.
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 由于专门的语言,术语和缩写,医疗保健文本数据存在独特的挑战.
- 域适应技术对于改进基于变压器的语言模型 (LMs) 在理解医疗文本方面至关重要.
- 关于医疗保健的LMs文献存在差距,特别是对于葡萄牙语等非英语语言.
研究的目的:
- 对医疗保健中的变压器进行域适应方法的范围文献审查.
- 调查医疗保健LM的发展,比较葡萄牙语与其他语言.
- 了解变压器在医疗保健信息提取 (IE) 中的有效性,并确定常用的提取医疗实体.
主要方法:
- 使用PRISMA-ScR方法的范围审查.
- 搜索了Scopus和科学网络核心集合数据库.
- 包括医疗保健LM或IE模型的研究;不包括大型语言模型 (LLM).
主要成果:
- 在137项检索的研究中,有60项符合纳入标准;没有发现系统性审查.
- 英语和中文在医疗保健LM开发方面处于领先地位,其中一些具有特定疾病的模型.
- 欧洲葡萄牙语缺乏公共医疗保健LM;变压器是IE常见的,命名实体识别是最受欢迎的任务. 诊断,剂量和症状是经常提取的实体.
结论:
- 域调整改善了医疗保健NLP下游任务性能.
- 医疗保健的变压器模型开发在英语和中文中更先进.
- 欧洲葡萄牙语需要集中开发一般和疾病特定的医疗保健LM,从其他语言学习以推进医疗保健中的AI.
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