We address the medication recommendation problem, which aims to recommend effective medications for a patient's current visit by utilizing information (e.g., diagnoses and procedures) given at the patient's current and past visits. While there exist a number of recommender systems designed for this problem, we point out that they are challenged in accurately capturing the relation (spec., the degree of relevance) between the current and each of the past visits for the patient when obtaining her current health status, which is the basis for recommending medications. To address this limitation, we propose a novel medication recommendation framework, named VITA, based on the following two novel ideas: (1) relevant-Visit selectIon; (2) Target-aware Attention. Through extensive experiments using real-world datasets, we demonstrate the superiority of VITA (spec., up to 5.56% higher accuracy, in terms of Jaccard, than the best competitor) and the effectiveness of its two core ideas. The code is available at https://github.com/jhheo0123/VITA.

提出了一种名为VITA的新型药物推荐框架，通过相关访问的选择和目标感知的注意机制，解决了现有推荐系统在获取患者当前健康状态时准确捕捉其与过去每次访问之间关系的挑战，并在真实世界数据集上进行了广泛实验，证明了VITA的卓越性能和其两个核心思想的有效性。

VITA: 医药推荐的精心选择和加权少量更佳