Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the growth of EMRs is at a lighting fast speed in the era of big data, content limitations in EMRs restrain the existed recommendation systems to reflect relevant medical facts, such as drug-drug interactions. Many medical knowledge graphs that contain drug-related information, such as DrugBank, may give hope for the recommendation systems. However, the direct use of these knowledge graphs in systems suffers from robustness caused by the incompleteness of the graphs. To address these challenges, we stand on recent advances in graph embedding learning techniques and propose a novel framework, called Safe Medicine Recommendation (SMR), in this paper. Specifically, SMR first constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly embeds diseases, medicines, patients, and their corresponding relations into a shared lower dimensional space. Finally, SMR uses the embeddings to decompose the medicine recommendation into a link prediction process while considering the patient's diagnoses and adverse drug reactions. Extensive experiments on real datasets are conducted to evaluate the effectiveness of proposed framework.

现有的大多数主要基于电子病历（EMR）的药物推荐系统都在极大地帮助医生做出更好的临床决策，使患者和护理人员受益。即使在大数据时代，电子病历的增长速度很快，但电子病历的内容限制仍然限制了现有的推荐系统，以反映相关的医学事实，例如药物相互作用。许多包含药物相关信息的医学知识图谱，例如DrugBank，可能会给推荐系统带来希望。但是，在系统中直接使用这些知识图会遭受图的不完整所导致的鲁棒性。为了应对这些挑战，我们站在图嵌入学习技术的最新进展中，并提出了一个新颖的框架，在本文中称为安全医学推荐（SMR）。具体而言，SMR首先通过将EMR（MIMIC-III）和医学知识图（ICD-9本体和DrugBank）桥接起来，构建高质量的异构图。然后，SMR将疾病，药物，患者及其对应关系共同嵌入一个共享的较低维度空间。最后，SMR使用嵌入将药物推荐分解为链接预测过程，同时考虑患者的诊断和药物不良反应。在真实数据集上进行了广泛的实验，以评估所提出框架的有效性。药品，患者及其对应关系进入一个共享的较低维度空间。最后，SMR使用嵌入将药物推荐分解为链接预测过程，同时考虑患者的诊断和药物不良反应。在真实数据集上进行了广泛的实验，以评估所提出框架的有效性。药品，患者及其对应关系进入一个共享的较低维度空间。最后，SMR使用嵌入将药物推荐分解为链接预测过程，同时考虑患者的诊断和药物不良反应。在真实数据集上进行了广泛的实验，以评估所提出框架的有效性。