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.

翻译：用药推荐是改善患者护理和减少不良事件的关键任务。然而，现有方法往往难以捕捉患者医疗记录、药物疗效与安全性以及药物相互作用（DDI）之间复杂动态的关系。本文提出ALGNet，一种新颖的模型，利用轻量图卷积网络（LGCN）和增强记忆网络（AMN）来提升用药推荐性能。LGCN能够高效地将患者记录和DDI图编码为低维嵌入，而AMN则通过记忆模块中的外部知识增强患者表征。我们在MIMIC-III数据集上评估了所提模型，结果表明其在推荐准确性和DDI规避方面优于多个基线模型。我们还进行了消融研究，以分析模型不同组件的影响。实验结果证明，ALGNet能以更少的计算量和更高的可解释性实现优越性能。本文代码详见：https://github.com/huyquoctrinh/ALGNet。