Leave No Patient Behind: Enhancing Medication Recommendation for Rare
  Disease Patients
Zihao Zhao Yi Jing Fuli Feng Jiancan Wu Chongming Gao Xiangnan He
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.