在临床命名实体识别中解决泛化问题:联合学习还是大型语言模型? :美国和英国眼科研究所对视觉敏度提取的案例研究
Quang N Nguyen1,2,3,4, Honghan Wu2,5, Nikolas Pontikos3,4
1Byers Eye Institute, Stanford University, Palo Alto, CA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
联合学习 (FL) 提高了跨机构的临床命名实体识别 (NER) 普遍性. 与结构化临床数据提取的大型语言模型 (LLM) 相比,FL,特别是STWT显示出更高的稳定性和准确性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 临床命名实体识别 (NER) 对于结构化临床文本数据至关重要.
- 确保NER模型在不同医疗机构中的通用性是一个重大挑战.
- 联合学习 (FL) 和大型语言模型 (LLM) 是临床NLP任务的新兴方法.
研究的目的:
- 为了比较联合学习 (FL) 和大型语言模型 (LLM) 对临床NER的有效性.
- 为了评估这些模型在从眼科笔记中提取视觉敏度 (VA) 的概括性,在两个不同的机构 (美国斯坦福大学和英国穆尔菲尔德斯眼科医院) 进行了评估.
主要方法:
- 使用基于BERT的模型为NER.
- 实施的联合学习策略:联合平均 (FedAvg) 和分层联合学习与加权训练 (STWT).
- 使用的大型语言模型:Llama-3-70B和Mixtral-8x7B.
- 从眼科笔记中对视觉敏度提取的评估模型性能.
主要成果:
- 联合学习显著提高了跨机构的模型通用性.
- 与FedAvg相比,STWT表现出优越的稳定性和准确性.
- 在摩尔菲尔德眼科医院的数据上,LLM的表现很好,但在结构化的斯坦福笔记上显示出局限性.
- 在跨机构的临床NER任务中,FL方法被证明更有效.
结论:
- 联合学习是一种高效的方法,可以提高临床NER模型的通用性.
- LLM 具有特定领域的局限性,并且对于所有结构化临床数据提取任务可能不是普遍最佳的.
- 需要量身定制的方法来应对跨机构临床NER的挑战.
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