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 the patient's state and symptoms over time. However, existing TCM recommendation approaches overlook the changes in patient status 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 the patient's condition across multiple visits. In addition, we incorporate an interaction knowledge graph to enhance the accuracy of recommendations by considering the interactions between different herbs and the patient's condition. Experimental results on a real-world dataset demonstrate that our approach outperforms existing TCM recommendation methods, achieving state-of-the-art performance.

翻译：传统中医（TCM）拥有利用天然草药治疗多种疾病的悠久历史。实践中，中医诊断与治疗具有高度个性化和整体有机性，需全面考虑患者随时间变化的状态与症状。然而，现有中医推荐方法忽略了患者状态的变化，仅探索症状与处方之间的潜在模式。本文提出一种新颖的时序状态演化交互知识图谱（SCEIKG）框架，该框架将模型视为一个序贯开方问题，通过考虑患者多次就诊期间的状态动态性。此外，我们融入交互知识图谱，通过考量不同草药与患者状态之间的相互作用来提升推荐准确性。在真实数据集上的实验结果表明，我们的方法优于现有中医推荐方法，实现了最先进的性能。