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.

提出了一种基于 EHR 数据的注意力引导协同决策网络 (ACDNet) 进行药物推荐的方法，ACDNet 利用注意力机制和 Transformer 模型，全面、局部地建模患者的健康状况和药物记录，并利用药物记录与药物表示之间的相似性促进推荐过程；实验结果表明，ACDNet 在 Jaccard、PR-AUC 和 F1 分数等指标上优于最先进的模型，并且消融实验进一步验证了 ACDNet 每个模块的有效性，实证了其在基于 EHR 数据的药物推荐中的实用性。

ACDNet：基于注意力引导的协作决策网络用于有效的药物推荐

ACDNet: Attention-guided Collaborative Decision Network for Effective  Medication Recommendation

Effectively representing medical concepts and patients is important for
healthcare analytical applications. Representing medical concepts for
healthcare analytical tasks requires incorporating medical domain knowledge and
prior information from patient description data. Current methods, such as
feature engineering and mapping medical concepts to standardized terminologies,
have limitations in capturing the dynamic patterns from patient description
data. Other embedding-based methods have difficulties in incorporating
important medical domain knowledge and often require a large amount of training
data, which may not be feasible for most healthcare systems. Our proposed
framework, MD-Manifold, introduces a novel approach to medical concept and
patient representation. It includes a new data augmentation approach, concept
distance metric, and patient-patient network to incorporate crucial medical
domain knowledge and prior data information. It then adapts manifold learning
methods to generate medical concept-level representations that accurately
reflect medical knowledge and patient-level representations that clearly
identify heterogeneous patient cohorts. MD-Manifold also outperforms other
state-of-the-art techniques in various downstream healthcare analytical tasks.
Our work has significant implications in information systems research in
representation learning, knowledge-driven machine learning, and using design
science as middle-ground frameworks for downstream explorative and predictive
analyses. Practically, MD-Manifold has the potential to create effective and
generalizable representations of medical concepts and patients by incorporating
medical domain knowledge and prior data information. It enables deeper insights
into medical data and facilitates the development of new analytical
applications for better healthcare outcomes.

MD-Manifold 是一种新颖的医学概念和患者表示法的框架，结合了数据增强方法、概念距离度量和患者网络，将关键的医学领域知识和先前数据信息纳入医学任务中，能够更准确地反映医学知识和患者特点，并在各种下游医学数据分析任务中优于其他现有技术。