Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation

TL;DR

This paper introduces SCEIKG, a novel framework for TCM recommendation that models patient condition dynamics over time and leverages herb-condition interactions, leading to improved accuracy.

Contribution

The paper presents a sequential, condition-aware knowledge graph approach for TCM recommendation, addressing the limitations of static pattern-based methods.

Findings

Outperforms existing TCM recommendation methods

Achieves state-of-the-art performance on real-world data

Effectively models patient condition evolution

Abstract

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.