  Medication recommendation is a vital task for improving patient care and
reducing adverse events. However, existing methods often fail to capture the
complex and dynamic relationships among patient medical records, drug efficacy
and safety, and drug-drug interactions (DDI). In this paper, we propose ALGNet,
a novel model that leverages light graph convolutional networks (LGCN) and
augmentation memory networks (AMN) to enhance medication recommendation. LGCN
can efficiently encode the patient records and the DDI graph into
low-dimensional embeddings, while AMN can augment the patient representation
with external knowledge from a memory module. We evaluate our model on the
MIMIC-III dataset and show that it outperforms several baselines in terms of
recommendation accuracy and DDI avoidance. We also conduct an ablation study to
analyze the effects of different components of our model. Our results
demonstrate that ALGNet can achieve superior performance with less computation
and more interpretability. The implementation of this paper can be found at:
https://github.com/huyquoctrinh/ALGNet.
