Large-scale Electronic Health Record (EHR) databases have become indispensable in supporting clinical decision-making through data-driven treatment recommendations. However, existing medication recommender methods often struggle with a user (i.e., patient) cold-start problem, where recommendations for new patients are usually unreliable due to the lack of sufficient prescription history for patient profiling. While prior studies have utilized medical knowledge graphs to connect medication concepts through pharmacological or chemical relationships, these methods primarily focus on mitigating the item cold-start issue and fall short in providing personalized recommendations that adapt to individual patient characteristics. Meta-learning has shown promise in handling new users with sparse interactions in recommender systems. However, its application to EHRs remains underexplored due to the unique sequential structure of EHR data. To tackle these challenges, we propose MetaDrug, a multi-level, uncertainty-aware meta-learning framework designed to address the patient cold-start problem in medication recommendation. MetaDrug proposes a novel two-level meta-adaptation mechanism, including self-adaptation, which adapts the model to new patients using their own medical events as support sets to capture temporal dependencies; and peer-adaptation, which adapts the model using similar visits from peer patients to enrich new patient representations. Meanwhile, to further improve meta-adaptation outcomes, we introduce an uncertainty quantification module that ranks the support visits and filters out the unrelated information for adaptation consistency. We evaluate our approach on the MIMIC-III and Acute Kidney Injury (AKI) datasets. Experimental results on both datasets demonstrate that MetaDrug consistently outperforms state-of-the-art medication recommendation methods on cold-start patients.

翻译：大规模电子健康记录（EHR）数据库通过数据驱动的治疗推荐，已成为支持临床决策不可或缺的工具。然而，现有的药物推荐方法常面临用户（即患者）冷启动问题，即由于缺乏足够的处方历史来构建患者画像，对新患者的推荐通常不可靠。尽管先前的研究利用医学知识图谱通过药理学或化学关系连接药物概念，但这些方法主要侧重于缓解项目冷启动问题，在提供适应个体患者特征的个性化推荐方面仍显不足。元学习在推荐系统中处理交互稀疏的新用户方面已显示出潜力。然而，由于EHR数据独特的序列结构，其在EHR中的应用仍待深入探索。为应对这些挑战，我们提出了MetaDrug，一个多层次、不确定性感知的元学习框架，旨在解决药物推荐中的患者冷启动问题。MetaDrug提出了一种新颖的两级元适应机制，包括：自适应，即利用患者自身的医疗事件作为支持集来适应新患者，以捕捉时间依赖性；以及同伴适应，即利用来自相似患者的就诊记录来适应模型，以丰富新患者的表征。同时，为进一步提升元适应效果，我们引入了一个不确定性量化模块，该模块对支持集中的就诊记录进行排序，并过滤掉不相关的信息，以确保适应过程的一致性。我们在MIMIC-III和急性肾损伤（AKI）数据集上评估了所提方法。两个数据集的实验结果表明，MetaDrug在冷启动患者上的性能持续优于当前最先进的药物推荐方法。