Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients

arXiv:2403.17745v1 Announce Type: new
Abstract: 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 …

abstract arxiv attention clinical cs.lg disease diseases drug combinations fairness healthcare however information patient patients recommendation recommendations recommendation systems systems type