UniMRE:一个统一的框架,用于使用大型语言模型进行零射击的医疗关系提取
Yunlong Li1, Pengcheng Wu1, Aoze Zheng1
1School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou, Henan 450001 China.
Health information science and systems
|July 14, 2025
概括
本研究介绍了UniMRE,这是一种使用大型语言模型 (LLM) 进行零射击医疗关系提取的新框架. UniMRE有效地提取了医疗关系三胞胎,克服了数据稀缺的挑战.
科学领域:
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 医学关系提取对于理解非结构化临床文本至关重要,但受到有限的标记数据的影响.
- 现有的零射击方法缺乏强大的域表示,阻碍了准确的关系提取.
- 大型语言模型 (LLM) 提供先进的上下文理解,对零射击任务有希望.
研究的目的:
- 引入UniMRE,一个统一的框架,用于零射击医疗关系提取,利用LLMs.
- 通过一种新的方法来解决医疗关系提取中标记数据的稀缺问题.
- 增强LLM在零射击环境中提取复杂医疗关系的能力.
主要方法:
- UniMRE采用知识注入策略,将医学专业知识整合到LLMs中.
- 它生成银标签,用于检索相关样本和关系规则.
- 一个关系提取代理处理检索的信息,根据信心评分改进标签.
主要成果:
- 与医疗数据集的基线模型相比,UniMRE表现优越.
- 该框架成功地在零射击设置中提取了关系三胞胎.
- 知识注入和标签精细化策略在提高准确性方面被证明是有效的.
结论:
- 通过利用LLMs,UniMRE提供了一种有效的解决方案,用于零射击的医疗关系提取.
- 拟议的方法增强了从非结构化文本中提取结构化医学知识的能力.
- 这一框架有可能显著推进医疗信息学和临床数据分析.
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