Traditional Chinese Medicine Epidemic Prevention and Treatment Question-Answering Model Based on LLMs

Background: Epidemic diseases in Traditional Chinese Medicine (TCM) constitute an essential part of Chinese medical science. TCM has accumulated rich theoretical and practical experiences in the prevention and treatment of epidemic diseases, forming the academic system of epidemic febrile disease, providing robust support for epidemic prevention and resistance in TCM. However, the numerous and complex literature on TCM epidemic diseases brings challenges to the organization and discovery of epidemic disease knowledges. Objective: To leverage the powerful knowledge learning ability of state-of-the-art LLMs (LLMs) to address the efficient acquisition and utilization of TCM epidemic disease knowledges. Methods: By collecting content related to epidemic diseases from 194 ancient TCM books, as well as the knowledge graph of TCM epidemic disease prevention and treatment, we built the large TCM epidemic disease model EpidemicCHAT based on the ChatGLM model. To assess the performances of the model, several open-source LLMs were compared in the study. Results: Compared to traditional LLMs, which may fail to answer or produce hallucinations in the field of TCM epidemic diseases, EpidemicCHAT demonstrates superior answering and reasoning abilities. In the evaluation of TCM epidemic disease prescription generation, the model achieved scores of 44.02, 61.10, and 59.40 on the BLEU-4, ROUGE-L, and METEOR metrics, respectively. Conclusion: The EpidemicCHAT model proposed in this study performs excellently in the field of TCM epidemic diseases, which might provide a reference for the construction of TCM LLMs and applications such as TCM auxiliary diagnosis and Chinese herbal prescription generation.