Multi-layer information fusion based on graph convolutional network for knowledge-driven herb recommendation

Prescription of Traditional Chinese Medicine (TCM) is a precious treasure accumulated in the long-term development of TCM. Artificial intelligence (AI) technology is used to build herb recommendation models to deeply understand regularities in prescriptions, which is of great significance to clinical application of TCM and discovery of new prescriptions. Most of herb recommendation models constructed in the past ignored the nature information of herbs, and most of them used statistical models based on bag-of-words for herb recommendation, which makes it difficult for the model to perceive the complex correlation between symptoms and herbs. In this paper, we introduce the properties of herbs as additional auxiliary information by constructing herb knowledge graph, and propose a graph convolution model with multi-layer information fusion to obtain symptom feature representations and herb feature representations with rich information and less noise. We apply the proposed model to the TCM prescription dataset, and the experiment results show that our model outperforms the baseline models in terms of Precision@5 by 6.2%, Recall@5 by 16.0% and F1-Score@5 by 12.0%. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The study leveraged AI technology to build herb recommendation models, introducing herb property information and proposing a multi-layer information fusion graph convolution model. The performance of the model outperformed baseline models, aiding in a deeper understanding of TCM prescriptions and exploration of new formulas.