Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation

Traditional Chinese Medicine (TCM) has a rich history of utilizing natural herbs to treat a diversity of illnesses. In practice, TCM diagnosis and treatment are highly personalized and organically holistic, requiring comprehensive consideration of patients' states and symptoms over time. However, existing TCM recommendation approaches overlook the changes in patients' states and only explore potential patterns between symptoms and prescriptions. In this paper, we propose a novel Sequential Condition Evolved Interaction Knowledge Graph (SCEIKG), a framework that treats the model as a sequential prescription-making problem by considering the dynamics of patients' conditions across multiple diagnoses. In addition, we incorporate an interaction knowledge graph to enhance the accuracy of recommendations by considering the interactions between different herbs and patients' conditions. Experimental results on the real-world dataset demonstrate that our approach outperforms existing TCM recommendation methods, achieving state-ofthe-art performance. Traditional Chinese Medicine (TCM) is an ancient and comprehensive system that has been integral to Chinese society for millennia (Cheung, 2011). TCM differs from Western medicine in light of its unique theoretical foundation, diagnosis methods, and treatment approaches, emphasizing the harmonious functioning of the body's structures (Zhang et al., 2015). Chinese Herbal Medicine, a key component of TCM, has gained global recognition for its positive impact on various illnesses. As a result, TCM recommendation systems, which assist physicians in making informed decisions about prescribing herbs, have emerged as crucial tools.