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：基于电子健康记录（EHR）的药物推荐因医疗数据的复杂性而面临挑战。现有方法通过提取患者EHR中的纵向信息来实现个性化推荐，但许多模型缺乏充分的患者表征能力，且忽视了患者用药记录与特定药物之间相似性的重要性。为此，本文提出了一种注意力引导的协作决策网络（ACDNet）用于药物推荐。具体而言，ACDNet利用注意力机制和Transformer，通过对患者历史就诊信息进行全局与局部建模，有效捕捉患者健康状况和用药记录。该网络还采用协作决策框架，通过计算用药记录与药物表征之间的相似度来优化推荐过程。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分数指标上均显著优于现有最优模型，验证了其优越性。此外，消融实验充分证明了ACDNet各模块的有效性，确认了其对整体性能的贡献。进一步的案例研究强化了ACDNet基于EHR数据进行药物推荐的有效性，展示了其在真实医疗场景中的实用价值。