LMGA: Lightweight multi-graph augmentation networks for safe medication recommendation

The rapid accumulation of large-scale electronic health records (EHRs) has witnessed the prosperity of intelligent medicine, such as medication recommendation (MR). However, most studies either fail to fully capture the structural correlation and temporal dependence among various medical records, or disregard the computational efficiency of the MR models. To fill this gap, we put forward a Lightweight Medication recommendation method which integrates bidirectional gate recurrent units (BiGRUs) with light graph convolutional networks (LGCNs) based on the multiple Graph Augmentation networks (LMGA). Specifically, BiGRUs are deployed to encode longitudinal visit histories and generate patient representations from a holistic perspective. Additionally, a memory network is constructed to extract local personalized features in the patients’ historical EHRs, and LGCNs are deployed to learn both drug co-occurrence and antagonistic relationships for integral drug representations with reduced computational resource requirements. Moreover, a drug molecular graph is leveraged to capture structural information and control potential DDIs in predicted medication combinations. Incorporating the representations of patients and medications, a lightweight and safe medication recommendation is available to promote prediction performance with reduced computational resource consumption. Finally, we conduct a series of experiments to evaluate the proposed LMGA on two publicly available datasets, and the experimental results demonstrate the superior performance of LMGA in MR tasks compared with the state-of-the-art baseline models.