TCM-KLLaMA:基于知识图和大型语言模型的传统中医药处方智能生成模型
Yi Zhuang1, Lingkai Yu1, Nan Jiang2
1School of Computer Science & Technology, Zhejiang Gongshang University, Hangzhou, PR China.
Computers in biology and medicine
|March 8, 2025
概括
这项研究介绍了TCM-KLLaMA,这是传统中医药 (TCM) 处方生成的先进模型. 它集成了TCM知识图表,以提高草药推的准确性和减少幻觉.
科学领域:
- 综合医学是一个整体的医学.
- 医疗保健中的人工智能
- 传统中国医学 (TCM) 是一种
背景情况:
- 传统中医 (TCM) 处方是患者治疗的基础,依赖于症状评估和草药组合.
- 目前用于处方生成的大型语言模型 (LLM) 主要使用症状数据,忽视了关键的TCM诊断方法,如舌头和脉冲分析.
- 基于LLM的方法容易产生幻觉,在医学应用中存在重大风险.
研究的目的:
- 为传统中医药 (TCM) 开发一种先进的处方生成模型,该模型包含TCM知识图 (KG).
- 通过减轻LLM幻觉来提高TCM处方生成的准确性和安全性.
- 改善将全面的TCM诊断信息整合到人工智能驱动的治疗建议中.
主要方法:
- 提出了TCM-KLLaMA模型,这是中国LLaMA2-7B模型的改编,具有新的输出层和损失函数.
- 开发了一个TCM知识图 (KG),包括症状,舌头诊断和脉诊断.
- 微调模型使用同义词和匹配知识注入 (SMKI) 机制来增强知识集成.
主要成果:
- 与基线模型相比,TCM-KLLaMA模型显示出更高的性能.
- 评估显示,对处方生成任务的精度和F1分数有显著的改善.
- 该模型有效地抑制了幻觉,并提高了推准确度.
结论:
- TCM-KLLaMA模型代表了人工智能辅助传统中医药 (TCM) 处方生成的重大进步.
- 整合TCM知识图和专门的微调机制可以提高模型的可靠性和准确性.
- 这种方法为生成TCM处方提供了更安全,更有效的方法,解决了传统LLM的局限性.
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