Context-Aware Safe Medication Recommendations with Molecular Graph and DDI Graph Embedding


  • Qianyu Chen Beijing Institute of Technology
  • Xin Li Beijing Institute of Technology
  • Kunnan Geng Beijing Institute of Technology
  • Mingzhong Wang The University of the Sunshine Coast



ML: Graph-based Machine Learning, DMKM: Graph Mining, Social Network Analysis & Community Mining, APP: Healthcare, Medicine & Wellness


Molecular structures and Drug-Drug Interactions (DDI) are recognized as important knowledge to guide medication recommendation (MR) tasks, and medical concept embedding has been applied to boost their performance. Though promising performance has been achieved by leveraging Graph Neural Network (GNN) models to encode the molecular structures of medications or/and DDI, we observe that existing models are still defective: 1) to differentiate medications with similar molecules but different functionality; or/and 2) to properly capture the unintended reactions between drugs in the embedding space. To alleviate this limitation, we propose Carmen, a cautiously designed graph embedding-based MR framework. Carmen consists of four components, including patient representation learning, context information extraction, a context-aware GNN, and DDI encoding. Carmen incorporates the visit history into the representation learning of molecular graphs to distinguish molecules with similar topology but dissimilar activity. Its DDI encoding module is specially devised for the non-transitive interaction DDI graphs. The experiments on real-world datasets demonstrate that Carmen achieves remarkable performance improvement over state-of-the-art models and can improve the safety of recommended drugs with a proper DDI graph encoding.




How to Cite

Chen, Q., Li, X., Geng, K., & Wang, M. (2023). Context-Aware Safe Medication Recommendations with Molecular Graph and DDI Graph Embedding. Proceedings of the AAAI Conference on Artificial Intelligence, 37(6), 7053-7060.



AAAI Technical Track on Machine Learning I