基于大语言模型的中医泄泻临床决策与处方生成研究OA

Objective To develop a clinical decision and prescription generation system(CDPGS)specif-ically for diarrhea in traditional Chinese medicine(TCM),utilizing a specialized large lan-guage model(LLM),Qwen-TCM-Dia,to standardize diagnostic processes and prescription generation. Methods Two primary datasets were constructed:an evaluation benchmark and a fine-tun-ing dataset consisting of fundamental diarrhea knowledge,medical records,and chain-of-thought(CoT)reasoning datasets.After an initial evaluation of 16 open-source LLMs across inference time,accuracy,and output quality,Qwen2.5 was selected as the base model due to its superior overall performance.We then employed a two-stage low-rank adaptation(LoRA)fine-tuning strategy,integrating continued pre-training on domain-specific knowledge with instruction fine-tuning using CoT-enriched medical records.This approach was designed to embed the clinical logic(symptoms → pathogenesis → therapeutic principles → prescrip-tions)into the model's reasoning capabilities.The resulting fine-tuned model,specialized for TCM diarrhea,was designated as Qwen-TCM-Dia.Model performance was evaluated for dis-ease diagnosis and syndrome type differentiation using accuracy,precision,recall,and F1-score.Furthermore,the quality of the generated prescriptions was compared with that of es-tablished open-source TCM LLMs. Results Qwen-TCM-Dia achieved peak performance compared to both the base Qwen2.5 model and five other open-source TCM LLMs.It achieved 97.05%accuracy and 91.48%F1-score in disease diagnosis,and 74.54%accuracy and 74.21%F1-score in syndrome type differ-entiation.Compared with existing open-source TCM LLMs(BianCang,HuangDi,LingDan,TCMLLM-PR,and ZhongJing),Qwen-TCM-Dia exhibited higher fidelity in reconstructing the"symptoms → pathogenesis → therapeutic principles → prescriptions"logic chain.It provid-ed complete prescriptions,whereas other models often omitted dosages or generated mis-matched prescriptions. Conclusion By integrating continued pre-training,CoT reasoning,and a two-stage fine-tun-ing strategy,this study establishes a CDPGS for diarrhea in TCM.The results demonstrate the synergistic effect of strengthening domain representation through pre-training and activating logical reasoning via CoT.This research not only provides critical technical support for the standardized diagnosis and treatment of diarrhea but also offers a scalable paradigm for the digital inheritance of expert TCM experience and the intelligent transformation of TCM.