RoKEPG: RoBERTa and Knowledge Enhancement for Prescription Generation of Traditional Chinese Medicine
This work addresses the challenge of assisting physicians in TCM diagnosis and treatment by enhancing prescription generation with domain-specific knowledge, though it is incremental as it builds on existing methods with a modest performance gain.
The paper tackled the problem of generating Traditional Chinese Medicine prescriptions from symptoms by proposing RoKEPG, a model that integrates TCM knowledge through an attention mask matrix, resulting in an improvement of about 2% in F1 score over the best baseline.
Traditional Chinese medicine (TCM) prescription is the most critical form of TCM treatment, and uncovering the complex nonlinear relationship between symptoms and TCM is of great significance for clinical practice and assisting physicians in diagnosis and treatment. Although there have been some studies on TCM prescription generation, these studies consider a single factor and directly model the symptom-prescription generation problem mainly based on symptom descriptions, lacking guidance from TCM knowledge. To this end, we propose a RoBERTa and Knowledge Enhancement model for Prescription Generation of Traditional Chinese Medicine (RoKEPG). RoKEPG is firstly pre-trained by our constructed TCM corpus, followed by fine-tuning the pre-trained model, and the model is guided to generate TCM prescriptions by introducing four classes of knowledge of TCM through the attention mask matrix. Experimental results on the publicly available TCM prescription dataset show that RoKEPG improves the F1 metric by about 2% over the baseline model with the best results.