Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation

The ancient art of Traditional Chinese Medicine (TCM) is distinguished by its extensive annals of utilizing nature's botanical emissaries to ameliorate a diverse array of pathological conditions. In practice, TCM diagnosis and treatment exhibit a pronounced degree of personalization and an organically holistic approach, necessitating a comprehensive consideration of the patient's evolving physiological and symptomatic states over temporal domains. However, existing methods for TCM recommendation represent a flaw in accounting for the dynamic oscillations in patients' conditions, instead restricting their explorations to potential correlative patterns between symptomatic presentations and prescribed remedies. In this paper, we propose a novel Sequential Condition Evolved Interaction Knowledge Graph (SCEIKG), an innovation framework that conceptualizes the method as a sequential prescription-making problem by considering the inherent dynamism of patients' conditions across multiple diagnoses. Furthermore, we incorporate an interaction knowledge graph to enhance the accuracy of recommendations by considering the intricate interplay between various herbs and patients' states. Experimental results on the real-world dataset demonstrate that our approach outperforms existing TCM recommendation methods, achieving state-of-the-art performance.