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.

提出了一种名为Robust and Accurate REcommendations for Medication (RAREMed)的新型模型，通过使用预训练-微调的学习范式和统一的输入序列方法来提高罕见疾病的准确性，同时引入了两个自监督预训练任务：序列匹配预测（SMP）和自重构（SR），以学习专门的药物需求和临床代码之间的相互关系，实验结果表明RAREMed为罕见和常见疾病患者提供准确的药物组合，从而减轻了药物推荐系统中的不公平性。

不放过任何一个患者：提升罕见疾病患者的药物推荐