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.

中文翻译：

---

不让任何患者掉队：加强对罕见病患者的药物推荐

药物推荐系统作为一种根据患者临床信息提供定制且有效的药物组合的手段，在医疗保健领域获得了极大的关注。然而，现有的方法常常存在公平性问题，因为与罕见疾病患者相比，对常见疾病患者的建议往往更准确。在本文中，我们提出了一种称为稳健且准确的药物推荐（RAREMed）的新模型，该模型利用预训练微调学习范例来提高罕见疾病的准确性。 RAREMed 采用具有统一输入序列方法的变压器编码器来捕获疾病和程序代码之间的复杂关系。此外，它还引入了两个自我监督的预训练任务，即序列匹配预测（SMP）和自我重建（SR），以学习专门的药物需求和临床代码之间的相互关系。两个真实世界数据集的实验结果表明，RAREMed 为罕见病和常见病患者提供了准确的药物组，从而减轻了药物推荐系统的不公平性。