ACDNet：用于有效药物推荐的注意力引导协同决策网络
ACDNet: Attention-guided Collaborative Decision Network for Effective Medication Recommendation
AI总结：
本文提出 ACDNet，结合注意力机制、Transformer 与协同决策框架建模患者历史就诊及用药-药物表示相似性，并在 MIMIC-III/IV 上验证其药物推荐性能优于现有方法。
摘要：
由于医疗数据复杂，利用电子健康记录（EHR）进行药物推荐具有挑战性。现有方法从患者的 EHR 中提取纵向信息，以实现个性化推荐。然而，现有模型往往缺乏充分的患者表示，并且忽视了考虑患者用药记录与特定药物之间相似性的重要性。因此，本文提出了一种用于药物推荐的注意力引导协同决策网络（Attention-guided Collaborative Decision Network，ACDNet）。具体而言，ACDNet 利用注意力机制和 Transformer，通过在全局和局部层面建模患者的历史就诊，有效捕捉患者健康状况和用药记录。ACDNet 还采用协同决策框架，利用用药记录与药物表示之间的相似性来促进推荐过程。在两个大型医疗数据集 MIMIC-III 和 MIMIC-IV 上的实验结果清楚表明，ACDNet 在 Jaccard、PR-AUC 和 F1 分数方面优于最先进模型，再次证实了其优越性。此外，消融实验为 ACDNet 中每个模块的有效性提供了有力证据，验证了它们对整体性能的贡献。进一步地，详细的案例研究强化了 ACDNet 在基于 EHR 数据进行药物推荐方面的有效性，展示了其在真实医疗场景中的实用价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
