OBJECTIVE To address the issue of recommending traditional Chinese medicine (TCM) prescriptions in specific fields, and to fully utilize the clinical records of lung cancer from TCM experts to automatically generate prescriptions, providing reference for the study of medication rules and TCM clinical decision-making assistance.

METHODS A TCM prescription recommendation algorithm was designed using the strong generative capabilities of large language models. This algorithm transformed clinical manifestations, standardized tongue diagnosis, and pulse diagnosis into TCM prescriptions through a large model, thereby converting the task of TCM prescription recommendation into a text generation task. The CHATGLM3 model, based on the GLM structure, was used to enhance the understanding of lung cancer cases and learn the intrinsic experiential knowledge of TCM experts in treating lung cancer, thereby improving the prescription generation effectiveness of the model. This was compared with traditional generative models.

RESULTS The study demonstrated that integrating TCM knowledge from lung cancer cases into large language models effectively improved the model's prescription generation capabilities. Particularly in generating commonly used core medications by TCM experts, the model showed a high tendency and provided rich and valuable reference information. The lung cancer TCM prescription recommendation model achieved 64.62% in BLEU, 55.78% in ROUGE, and 47.39% in METEOR scores. It also achieved accuracies of 67.79%, 63.66%, 56.76%, and 51.93% in the top 5, 10, 15, and 20 TCM prescriptions, respectively, outperforming the baseline model.

CONCLUSION The lung cancer TCM prescription recommendation model presented in this paper achieves better prescription generation results compared to traditional generative models. It demonstrates the model's ability to learn knowledge about lung cancer diagnosis and treatment from cases, thereby generating TCM prescriptions that align with TCM treatment principles. This also provides a potential direction for future assistance in clinical decision-making.