National Pilot School of Software, Yunnan University, Kunming 650091, China.
National Pilot School of Software, Yunnan University, Kunming 650091, China.
Neural Netw. 2022 Feb;146:1-10. doi: 10.1016/j.neunet.2021.11.010. Epub 2021 Nov 16.
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%.
中医处方是中医长期发展中积累的宝贵财富。利用人工智能（AI）技术构建草药推荐模型，深入了解处方中的规律，对中医的临床应用和新处方的发现具有重要意义。过去构建的大多数草药推荐模型都忽略了草药的自然信息，而且大多数模型都使用基于词袋的统计模型进行草药推荐，这使得模型难以感知症状和草药之间的复杂相关性。在本文中，我们通过构建草药知识图谱，将草药的性质信息作为附加辅助信息引入，并提出了一种具有多层信息融合的图卷积模型，以获得具有丰富信息和较少噪声的症状特征表示和草药特征表示。我们将所提出的模型应用于中医处方数据集，实验结果表明，我们的模型在 Precision@5 方面比基线模型高出 6.2%，在 Recall@5 方面高出 16.0%，在 F1-Score@5 方面高出 12.0%。
