知识注入式提示:用大型语言模型评估和推进临床文本数据生成
Findings of ACL. ACL
|March 2, 2026
概括
本研究介绍了ClinGen,这是一种使用大型语言模型 (LLM) 生成合成临床文本的新方法. 通过克服隐私和资源限制,ClinGen提高了临床自然语言处理 (NLP) 的性能.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 临床自然语言处理 (NLP) 被复杂的医学术语和背景所阻碍.
- 大型语言模型 (LLM) 显示出潜力,但面临着直接临床应用的隐私和资源限制.
研究的目的:
- 为临床NLP任务使用LLM生成合成临床文本开发一种资源高效的方法.
- 为解决与直接在医疗保健中部署LLM相关的隐私问题和资源限制.
主要方法:
- 提出了ClinGen,这是一个创新的方法,结合了临床知识提取和上下文信息的LLM提示.
- 利用特定领域的知识图和LLM来指导临床主题和写作风格的生成.
- 在合成数据生成方面采用了资源高效的策略.
主要成果:
- 在8个临床NLP任务和18个数据集中,ClinGen表现出一致的性能提升,平均为7.7%-8.7%.
- 生成的合成数据有效地与真实临床数据集的分布保持一致.
- ClinGen丰富了培训实例的多样性,改善了模型的概括性.
结论:
- ClinGen为合成临床文本生成提供了有效和高效的解决方案.
- 该方法成功地减轻了临床NLP中的隐私和资源挑战.
- 在各种临床NLP应用中,ClinGen显著提高了性能和数据多样性.
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