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：中医药（TCM）有着利用天然草药治疗多种疾病的悠久历史。在实践中，中医诊断和治疗高度个性化且具有有机整体性，需要综合考虑患者随时间变化的状态和症状。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。本文提出了一种新颖的序列条件演化交互知识图谱（SCEIKG）框架，该框架将模型视为一个序贯处方生成问题，通过考虑多次就诊中患者病情的变化。此外，我们引入交互知识图谱，通过考虑不同草药与患者病情之间的相互作用来提升推荐准确性。在真实世界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，达到了最先进的性能。