METHODS: This study harnessed a wide array of TCM data resources, including TCM ancient books, textbooks, and clinical data, to create 3 key datasets: the TCM Pre-trained Dataset, the Traditional Chinese Patent Medicine (TCPM) Question Answering Dataset, and the Spleen and Stomach Herbal Prescription Recommendation Dataset. These datasets underpinned the development of the Lingdan Pre-trained LLM and 2 specialized models: the Lingdan-TCPM-Chat Model, which uses a Chain-of-Thought process for symptom analysis and TCPM recommendation, and a Lingdan Prescription Recommendation model (Lingdan-PR) that proposes herbal prescriptions based on electronic medical records.
RESULTS: The Lingdan-TCPM-Chat and the Lingdan-PR Model, fine-tuned on the Lingdan Pre-trained LLM, demonstrated state-of-the art performances for the tasks of TCM clinical knowledge answering and herbal prescription recommendation. Notably, Lingdan-PR outperformed all state-of-the-art baseline models, achieving an improvement of 18.39% in the Top@20 F1-score compared with the best baseline.
CONCLUSIONS: This study marks a pivotal step in merging advanced LLMs with TCM, showcasing the potential of artificial intelligence to help improve clinical decision-making of medical diagnostics and treatment strategies. The success of the Lingdan Pre-trained LLM and its derivative models, Lingdan-TCPM-Chat and Lingdan-PR, not only revolutionizes TCM practices but also opens new avenues for the application of artificial intelligence in other specialized medical fields. Our project is available at https://github.com/TCMAI-BJTU/LingdanLLM.
方法:本研究利用了多种中医数据资源,包括中医古籍,教科书,和临床数据,创建3个关键数据集:TCM预训练数据集,中成药(TCPM)问答数据集,和脾胃草药处方推荐数据集。这些数据集支持了LingdanPre-trainedLLM和2个专门模型的开发:Lingdan-TCPM-Chat模型,它使用思想链过程进行症状分析和TCPM推荐,以及基于电子病历提出草药处方的灵丹处方推荐模型(灵丹-PR)。
结果:Lingdan-TCPM-Chat和Lingdan-PR模型,对Lingdan预培训LLM进行微调,展示了中医临床知识回答和草药处方推荐任务的最新表现。值得注意的是,Lingdan-PR的表现优于所有最先进的基线模型,与最佳基线相比,Top@20F1评分提高了18.39%。
结论:这项研究标志着将先进的LLM与TCM合并的关键一步,展示人工智能的潜力,以帮助改善医疗诊断和治疗策略的临床决策。灵丹预训练LLM及其衍生模型的成功,Lingdan-TCPM-Chat和Lingdan-PR,不仅彻底改变了中医实践,而且为人工智能在其他专业医学领域的应用开辟了新途径。我们的项目可在https://github.com/TCMAI-BJTU/LingdanLLM上获得。