Relation-aware pre-trained network with hierarchical aggregation mechanism for cold-start drug recommendation

Drug recommendation systems have garnered considerable interest in the healthcare, striving to offer precise and customized drug prescriptions that align with patients' specific health needs. However, existing methods primarily focus on modeling temporal dependencies between visits for patients with multiple encounters, often neglecting the challenge of data sparsity in single-visit patients. To address above limitation, we propose a novel Relation-aware Pre-trained Network with hierarchical aggregation mechanism for drug recommendation (RPNet), which employs a pre-training and fine-tuning framework to enhance drug recommendation in cold-start scenario. Specifically, we introduce: 1) A code matching discrimination task during pre-training, designed to model the complex relationships between diagnosis and procedure entities. This task employs a mask-replace contrastive learning strategy, which pulls similar samples closer while pushing dissimilar ones apart, thereby capturing robust feature representations; 2) A hierarchical aggregation mechanism that enhances drug information integration by first selecting relevant visits based on rarity discrimination and then retrieving similar patients' drug insights via similarity matching during fine-tuning. Extensive experiments on two real-world datasets demonstrate the superiority of the proposed RPNet, notably improving the F1 metric by 1.32% and 1.19%. The code of our model is available at https://github.com/Lxb0102/RPNet.