Abstract
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: this https URL.
Abstract (translated)
药物推荐是提高患者护理和减少不良事件的关键任务。然而，现有的方法通常无法捕捉患者医疗记录、药物效力和安全性以及药物-药物相互作用（DDI）之间的复杂和动态关系。在本文中，我们提出了ALGNet，一种新模型，它利用光图卷积网络（LGCN）和增强记忆网络（AMN）来增强药物推荐。LGCN可以有效地将患者记录和DDI图编码为低维嵌入，而AMN可以利用外部记忆模块的外部知识来增强患者表示。我们在MIMIC-III数据集上评估我们的模型，并证明了其在推荐准确性和DDI避免方面的性能优于几个基线。我们还进行了消融研究，以分析我们模型中不同组件的影响。本文的结果表明，ALGNet可以在更少的计算和更高的可解释性下实现卓越的性能。该论文的实施可以从以下链接找到：https://this URL。
URL
https://arxiv.org/abs/2312.08377
