使用大型语言模型,利用合成数据和本体学促进临床信息提取
Yan Hu1, Huan He2, Qingyu Chen2
1Mcwilliam School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
大型语言模型可以生成合成临床数据,以改善命名实体识别. 自我验证和语义映射增强了数据实用性,人类与合成数据的比例为1:1,优化了性能.
科学领域:
- 自然语言处理自然语言处理.
- 医疗保健中的人工智能
背景情况:
- 电子健康记录包含了大量的非结构化的临床文本.
- 开发信息提取系统至关重要,但被稀缺的注释数据所限制.
研究的目的:
- 探索大型语言模型,用于生成用于命名实体识别的合成临床数据.
- 评估合成数据对模型性能和通用性的影响.
主要方法:
- 一个使用自验证合成数据生成与SNOMED-CT语义映射的新框架.
- 利用GPT-4o-mini进行数据创建和LLaMA-3-8B进行微调.
- 代验证和异常检测以完善合成数据质量.
主要成果:
- 自验证和语义映射显著提高了合成数据的实用性.
- 人类注释与合成数据的1:1比率产生了最佳的性能增长.
- 在四个不同的临床数据集中观察到更好的模型通用性.
结论:
- 合成数据生成是临床NLP注释挑战的可扩展解决方案.
- 平衡人类和合成数据是提高模型性能的关键.
- 拟议的框架提高了临床信息提取能力.
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