TCM-KLLaMA: Intelligent generation model for Traditional Chinese Medicine Prescriptions based on knowledge graph and large language model.

Traditional Chinese medicine (TCM) prescriptions are a basic component of TCM treatment, developed by assessing patient symptoms and prescribing a mix of herbs. Accurate prescription generation is critical for enhancing treatment outcomes and maintaining patient safety. However, conventional methods based on Large Language Models (LLMs) focus mainly on symptom information, neglecting other TCM diagnostic expertise, such as tongue and pulse diagnosis, and are prone to hallucination, which is unacceptable in medical applications. To address these challenges, the paper proposes an effective prescription generation model enriched by a TCM knowledge graph (KG) called the TCM-KLLaMA model. In this model, the Chinese-LLaMA2-7 B model is provided with a new output layer and loss function to suppress hallucinations and increase recommendation accuracy. A TCM KG including symptoms, tongue diagnosis, and pulse diagnosis was developed, and the model was fine-tuned utilizing the suggested synonym and matching knowledge injection (SMKI) mechanism. Extensive experiments demonstrate that the TCM- KLLaMA outperforms baseline models in both Precision and F1 Score, proving its superior performance in prescription generation tasks.