对于自动LOINC文档本体学的NLP模型的比较分析在临床注释中命名实体识别标题标题
Annie E Bowles1,2, Qiwei Gan1,2, Elizabeth Hanchrow1,2
1VA Salt Lake City Health Care System.
Studies in health technology and informatics
|August 8, 2025
概括
确定研究相关的临床注释至关重要. 我们测试了用于将笔记映射到LOINC文档构造学 (LDO) 的模型,发现BERT表现最好,而大型语言模型 (LLM) 显示出希望.
科学领域:
- 临床信息学是一种临床信息学.
- 自然语言处理自然语言处理.
- 生物医学信息学是生物医学信息学.
背景情况:
- 临床笔记对于研究至关重要,但包含各种笔记类型.
- 使用LOINC文档本体学 (LDO) 标准化笔记类型可以提高数据实用性.
- 需要自动化方法来有效地将临床笔记映射到LDO.
研究的目的:
- 评估用于自动识别LOINC文档本体学实体在退伍军人事务 (VA) 临床笔记标题中的模型.
- 将受监督的BERT模型与开源大语言模型 (LLM) 的性能进行比较.
主要方法:
- 用三个不同的模型进行了实验,用于自动化LOINC DO实体识别.
- 使用VA笔记标题作为模型测试的主要数据源.
- 监管BERT和开源LLM没有微调的性能指标的比较.
主要成果:
- 监管的BERT模型在识别LOINC DO实体方面表现出卓越的表现.
- 开源大型语言模型 (LLM) 即使没有特定的微调,也取得了竞争性结果.
- 该研究强调了LLM在临床笔记分类中的潜力.
结论:
- 自动映射到LOINC文档本体学有助于为研究选择临床笔记.
- 伯特模型提供了一个强大的解决方案,而LLM则为此任务提供了一个有前途的替代方案.
- 未来的研究应该探索微调LLM,并纳入额外的数据,以提高分类准确性.
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