在大型语言模型中使用SNOMED CT:范围审查
1Republic of Korea Air Force Aerospace Medical Center, Cheongju, Republic of Korea.
JMIR medical informatics
|October 7, 2024
概括
将SNOMED CT与大型语言模型 (LLM) 集成在生物医学NLP任务中显示出前景. 然而,不同的整合方法和报告限制了关于有效性的最终结论.
科学领域:
- 生物医学自然语言处理 (NLP)
- 医疗保健中的人工智能
- 知识表示和推理.
背景情况:
- 大型语言模型 (LLM) 在一般NLP中表现出色,但在专门的生物医学领域面临挑战.
- 整合生物医学知识,如SNOMED CT (医学系统化命名 - 临床术语),可能会提高LLM的绩效.
- 缺乏对SNOMED CT整合方法和LLMs有效性的系统审查.
研究的目的:
- 检查SNOMED CT如何集成到LLMs中.
- 识别用于集成的LLM类型和SNOMED CT组件.
- 评估SNOMED CT集成对NLP任务中LLM绩效的影响.
主要方法:
- 根据PRISMA-ScR指南进行了范围审查.
- 搜索了主要的科学数据库 (ACM,ACL,IEEE,PubMed,Embase) 来查找从2018年到2023年的研究.
- 提取和合成了关于LLM类型,SNOMED CT集成策略,终端任务和性能指标的数据.
主要成果:
- 包括37项研究,其中双向编码器表示从变压器 (BERT) 变体是常见的LLMs.
- 确定了三个整合方法:输入增强 (76%),融合模块 (14%) 和外部检索 (14%).
- 大多数研究报告了性能改善 (89%),特别是在医疗概念规范化 (41%),但收益差异很大,直接比较有限 (51%).
结论:
- 有多种方法可以将SNOMED CT集成到LLM中,主要是使用概念描述.
- 虽然有希望,但不一致的评估阻碍了关于有效性的最终结论.
- 未来的研究需要标准化的报告,先进的整合技术和统一的评估框架.
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