# Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network

arXiv: https://arxiv.org/abs/2002.08575 · HTML: https://mlsanity.com/paper/2002.08575/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering](https://mlsanity.com/paper/2112.07191/) (2021)
- [TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescription Prediction](https://mlsanity.com/paper/2407.10510/) (2024)
- [Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation](https://mlsanity.com/paper/2305.17866/) (2023)
- [GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation](https://mlsanity.com/paper/2111.10342/) (2021)
- [ResMGCN: Residual Message Graph Convolution Network for Fast Biomedical Interactions Discovering](https://mlsanity.com/paper/2311.07632/) (2023)
- [Decision Support System for Chronic Diseases Based on Drug-Drug Interactions](https://mlsanity.com/paper/2303.02405/) (2023)
- [FMASH: Advancing Traditional Chinese Medicine Formula Recommendation with Efficient Fusion of Multiscale Associations of Symptoms and Herbs](https://mlsanity.com/paper/2503.05167/) (2025)
- [A Hierarchical Structure-Enhanced Personalized Recommendation Model for Traditional Chinese Medicine Formulas Based on KG Diffusion Guidance](https://mlsanity.com/paper/2509.23560/) (2025)

## Abstract (arXiv)

Herb recommendation plays a crucial role in the therapeutic process of Traditional Chinese Medicine(TCM), which aims to recommend a set of herbs to treat the symptoms of a patient. While several machine learning methods have been developed for herb recommendation, they are limited in modeling only the interactions between herbs and symptoms, and ignoring the intermediate process of syndrome induction. When performing TCM diagnostics, an experienced doctor typically induces syndromes from the patient's symptoms and then suggests herbs based on the induced syndromes. As such, we believe the induction of syndromes, an overall description of the symptoms, is important for herb recommendation and should be properly handled. However, due to the ambiguity and complexity of syndrome induction, most prescriptions lack the explicit ground truth of syndromes. In this paper, we propose a new method that takes the implicit syndrome induction process into account for herb recommendation. Given a set of symptoms to treat, we aim to generate an overall syndrome representation by effectively fusing the embeddings of all the symptoms in the set, to mimic how a doctor induces the syndromes. Towards symptom embedding learning, we additionally construct a symptom-symptom graph from the input prescriptions for capturing the relations between symptoms; we then build graph convolution networks(GCNs) on both symptom-symptom and symptom-herb graphs to learn symptom embedding. Similarly, we construct a herb-herb graph and build GCNs on both herb-herb and symptom-herb graphs to learn herb embedding, which is finally interacted with the syndrome representation to predict the scores of herbs. In this way, more comprehensive representations can be obtained. We conduct extensive experiments on a public TCM dataset, showing significant improvements over state-of-the-art herb recommendation methods.