  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 the 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. To our best knowledge, SMR
is the first to learn embeddings of a patient-disease-medicine graph for
medicine recommendation in the world. Extensive experiments on real datasets
are conducted to evaluate the effectiveness of proposed framework.
