2 research outputs found
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 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