大型语言模型在令牌级临床命名实体识别中扎

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Qiuhao Lu¹, Rui Li¹, Andrew Wen¹

¹McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.

AMIA ... Annual Symposium proceedings. AMIA Symposium

|May 26, 2025

概括

大型语言模型 (LLM) 对罕见疾病命名实体识别 (NER) 有希望. 本研究探讨了用于代币级临床NER的本地和专有LLM,确定了挑战和改进.

科学领域:

医疗保健信息学医疗保健信息学
人工智能的人工智能
临床自然语言处理临床自然语言处理

背景情况:

大型语言模型 (LLM) 在医疗保健领域具有潜力,特别是在具有数据挑战的罕见疾病方面.
命名实体识别 (NER) 对于提取临床信息至关重要,但目前的LLM研究重点是文档级的NER.
在使用本地开源LLMs的代币级临床NER中存在差距.

研究的目的:

调查专有和本地LLM对代币级临床NER的有效性.
为应对罕见病文本分析数据稀缺性和复杂性的挑战.
探索不同的LLM应用方法,包括提示和微调.

主要方法:

使用零射击提示,少射击提示,检索增强生成 (RAG) 和指令微调的实验.
对标记级临床NER任务的专有和本地LLM的评估.
专注于临床文本,特别是与罕见疾病相关的文本.

主要成果:

在代币级的NER中,LLM面临着固有的挑战,特别是在罕见疾病领域.
该研究确定了LLM在精确的临床实体提取中遇到的特定困难.
获得了对LLM在医疗保健NER应用中的潜在改进的见解.

结论:

使用LLM,特别是本地模型的代币级临床NER存在重大挑战.
为了在罕见疾病信息学中有效部署LLM,需要进一步的研究和模型改进.
这项研究有助于在专业医疗保健环境中推进LLM应用.

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