Medication recommendation systems have gained significant attention in healthcare as a means of providing tailored and effective drug combinations based on patients' clinical information. However, existing approaches often suffer from fairness issues, as recommendations tend to be more accurate for patients with common diseases compared to those with rare conditions. In this paper, we propose a novel model called Robust and Accurate REcommendations for Medication (RAREMed), which leverages the pretrain-finetune learning paradigm to enhance accuracy for rare diseases. RAREMed employs a transformer encoder with a unified input sequence approach to capture complex relationships among disease and procedure codes. Additionally, it introduces two self-supervised pre-training tasks, namely Sequence Matching Prediction (SMP) and Self Reconstruction (SR), to learn specialized medication needs and interrelations among clinical codes. Experimental results on two real-world datasets demonstrate that RAREMed provides accurate drug sets for both rare and common disease patients, thereby mitigating unfairness in medication recommendation systems.

翻译：药物推荐系统作为一种基于患者临床信息提供个性化有效药物组合的手段，在医疗领域获得了显著关注。然而，现有方法常存在公平性问题：针对常见病患者的推荐准确率通常高于罕见病患者。本文提出了一种名为RAREMed（面向药物推荐的稳健准确模型）的新型模型，该模型利用预训练-微调学习范式提升对罕见病的推荐准确性。RAREMed采用统一输入序列方式的Transformer编码器，以捕捉疾病和手术代码间的复杂关联。此外，该模型引入两个自监督预训练任务——序列匹配预测（SMP）与自重建（SR），以学习药物专需知识及临床代码间的相互作用。在两个真实数据集上的实验结果表明，RAREMed能为罕见病患者和常见病患者均提供准确的药物组合，从而缓解药物推荐系统中的不公平现象。