mlsanityData to 14 Oct 2026

Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation

arXiv 2305.17866 ↗First posted 29 May 2023cs.AI cs.IRcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 16 of its 36 references that are in the map. First posted on arXiv 29 May 2023.

Builds on

16 of 36 references.

References · 16

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4 more
  1. 2022Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learningcited by 10
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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescription Predictioncited by 4
  2. 2025FMASH: Advancing Traditional Chinese Medicine Formula Recommendation with Efficient Fusion of Multiscale Associations of Symptoms and Herbscited by 0