Abstract: In recent years, drug recommendation based on deep learning models has been extensively studied and widely applied in the field of wisdom medicine. This paper proposes a drug recommendation model with dual graph convolutional network based on graph embedding. Firstly, it constructs the knowledge graph of patient’s attributes and medications. The embedding representation is obtained using graph embedding. Secondly, the embedding representation of the knowledge graph of patient’s attributes are put into the multilevel graph attention network layer loaded with attention mechanism and bidirectional propagation mechanism for disseminating and aggregating information. Then, the representation of patient’s attributes and the embedded representation of the knowledge graph of patient’s medications are integrated. They are put into the multilevel graph attention network layer training again to mine the high-level association between patient’s attributes and medications. Finally, the drug recommendation is completed. It carries out an empirical study with the basic patient’s information, physiological characteristics and patient’s medication data in the data set of the medical information mart for intensive care Ⅳ as the objects. The experimental results prove that it outperforms the baseline method in four evaluation indexes：precision, recall, F1 score and NDCG.

Key words: wisdom medicine, knowledge graph, graph convolutional network, drug recommendation

摘要： 近年来，基于深度学习模型的药物推荐在智慧医疗领域得到了广泛的研究和应用。提出了一种基于图嵌入的双层图卷积网络药物推荐模型。构建患者属性知识图谱和患者用药知识图谱，利用图嵌入生成嵌入表示，将患者属性知识图谱的嵌入表示放入加载了注意力机制和双向传播机制的多层图注意力网络层进行信息传播与融合，将得到患者的特征表示和患者用药知识图谱嵌入表示进行聚合，再次放入多层图注意力网络层训练，从而挖掘患者属性和患者用药之间的高阶关联，最终完成药物推荐。以重症监护医学信息数据库数据集中的患者基本信息、生理特征和患者用药数据作为对象开展实证研究。实验结果证明，其在推荐准确率、召回率、F1分数和NDCG四个评价指标上均优于基线方法。

关键词: 智慧医疗, 知识图谱, 图卷积网络, 药物推荐