GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination


  • Junyuan Shang Peking University
  • Cao Xiao IBM Research
  • Tengfei Ma IBM Research
  • Hongyan Li Peking University
  • Jimeng Sun Georgia Institute of Technology



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.




How to Cite

Shang, J., Xiao, C., Ma, T., Li, H., & Sun, J. (2019). GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 1126-1133.



AAAI Technical Track: Applications