  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.
