A Sequential Condition Evolution and Interaction Knowledge Graph Framework 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 various pathological conditions. 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 approach for optimizing TCM recommendations based on Sequential Condition Evolution and Interaction Knowledge Graphs 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. The source code of SCEIKG is available at https://github.com/jzephyrl/SCEIKG-TCM .