  Recent progress in deep learning is revolutionizing the healthcare domain
including providing solutions to medication recommendations, especially
recommending medication combination for patients with complex health
conditions. Existing approaches either do not customize based on patient health
history, or ignore existing knowledge on drug-drug interactions (DDI) that
might lead to adverse outcomes. To fill this gap, we propose the Graph
Augmented Memory Networks (GAMENet), which integrates the drug-drug
interactions knowledge graph by a memory module implemented as a graph
convolutional networks, and models longitudinal patient records as the query.
It is trained end-to-end to provide safe and personalized recommendation of
medication combination. We demonstrate the effectiveness and safety of GAMENet
by comparing with several state-of-the-art methods on real EHR data. GAMENet
outperformed all baselines in all effectiveness measures, and also achieved
3.60% DDI rate reduction from existing EHR data.
