Medication recommendation using Electronic Health Records (EHR) is challenging due to complex medical data. Current approaches extract longitudinal information from patient EHR to personalize recommendations. However, existing models often lack sufficient patient representation and overlook the importance of considering the similarity between a patient's medication records and specific medicines. Therefore, an Attention-guided Collaborative Decision Network (ACDNet) for medication recommendation is proposed in this paper. Specifically, ACDNet utilizes attention mechanism and Transformer to effectively capture patient health conditions and medication records by modeling their historical visits at both global and local levels. ACDNet also employs a collaborative decision framework, utilizing the similarity between medication records and medicine representation to facilitate the recommendation process. The experimental results on two extensive medical datasets, MIMIC-III and MIMIC-IV, clearly demonstrate that ACDNet outperforms state-of-the-art models in terms of Jaccard, PR-AUC, and F1 score, reaffirming its superiority. Moreover, the ablation experiments provide solid evidence of the effectiveness of each module in ACDNet, validating their contribution to the overall performance. Furthermore, a detailed case study reinforces the effectiveness of ACDNet in medication recommendation based on EHR data, showcasing its practical value in real-world healthcare scenarios.

翻译：利用电子健康记录进行用药推荐因医疗数据复杂而极具挑战性。现有方法通过提取患者的纵向信息以实现个性化推荐，但现有模型常缺乏充分的患者表征，且忽视了患者用药记录与特定药物间相似性的重要性。为此，本文提出一种基于注意力引导的协同决策网络用于用药推荐。具体而言，ACDNet通过注意力机制与Transformer，在全局和局部层级建模患者历史就诊记录，从而有效捕捉患者健康状况与用药记录。该网络还采用协同决策框架，利用用药记录与药物表征之间的相似性优化推荐过程。在MIMIC-III和MIMIC-IV两个大规模医疗数据集上的实验结果表明，ACDNet在Jaccard系数、PR-AUC和F1分数上均显著优于现有最优模型，证实了其优越性。此外，消融实验为ACDNet各模块的有效性提供了坚实证据，验证了它们对整体性能的贡献。进一步的案例研究强化了ACDNet基于电子健康记录进行用药推荐的有效性，展示了其在真实医疗场景中的实际应用价值。