Combating the Bucket Effect:Multi-Knowledge Alignment for Medication Recommendation

Combating the Bucket Effect:Multi-Knowledge Alignment for Medication Recommendation Xiang Li a,, Haixu Ma a,, Guanyong Wu a, Shi Mu a, Chen Li a, and Shunpan Liang a,b, a School of Information Science and Engineering, Yanshan University, Qin Huangdao, 066004, China b Xinjiang College Of Science & Technology, Korla, 841000, ChinaA R T I C L E I N F OKeywords: Medication Recommendation Molecular Representation Learning A B S T R A C T Medication recommendation is crucial in healthcare, offering effective treatments based on patient's electronic health records (EHR). Previous studies show that integrating more medication-related knowledge improves medication representation accuracy. However, not all medications encompass multiple types of knowledge data simultaneously. For instance, some medications provide only textual descriptions without structured data. This imbalance in data availability limits the performance of existing models, a challenge we term the "bucket effect" in medication recommendation. To fill this gap, we introduce a cross-modal medication encoder capable of seamlessly aligning data from different modalities and propose a medication recommendation framework to integrate Multiple types of K nowledge, named MKMed. Then, we combine the multi-knowledge medication representations with patient records for recommendations. Extensive experiments on the MIMIC-III and MIMIC-IV datasets demonstrate that MKMed mitigates the "bucket effect" in data, and significantly outperforms state-of-the-art baselines in recommendation accuracy and safety.1. Introduction Given the increasing demand for healthcare resources, there is a growing emphasis on AI-based medical systems. Medication recommendations Shang, Xiao, Ma, Li and Sun (2019); Wu, Qiu, Jiang, Qi and Wu (2022); Li, Liang, Hou and Ma (2024a), as a key area, aim to integrate clinical knowledge with patient electronic health records (EHR), enhancing the accuracy, safety, and efficiency of clinical decision-making for patients. Existing methods can be divided into two categories. The first category focuses on exploring the complex relationships between multiple medical events, optimizing patient representation by constructing complex networksLe, Tran and Venkatesh (2018); Jin, Yang, Sun, Liu, Qu and Tong (2018); Zheng, Wang, Xu, Shen, Qin, Huai, Liu and Chen (2021). For example, RAREMed Zhao, Jing, Feng, Wu, Gao and He (2024) focuses on the connections between rare events and others.