E-HMFNet: A Knowledge-Enhanced Hierarchical Molecular Representation Fusion Network for Drug Recommendation
Junjie Zhang, Xuan Zang, Hao Chen, Buzhou Tang · 2023
Combinatorial drug recommendation involves recommending appropriate drug combinations for patients based on their complex health conditions, which is an essential task for AI in healthcare. However, existing approaches have several limitations. Firstly, they fail to fully utilize important information such as the hierarchical structure of drug molecules, patient visit history, and prior medical knowledge. Secondly, they ignore the inherent associations between these pieces of information and only encode one or two of them in isolation, leading to sub-optimal results. To address these issues, we propose KE-HMFNet, which leverages patient visit history, hierarchical molecular representation of drugs, and prior medical knowledge, and explicitly models their inherent association to make medication recommendations that are both effective and safe. Specifically, we develop a patient-guided fusion mechanism to make the hierarchical molecular representation disease-relevant and substructure-aware. Additionally, we design a knowledge-enhanced medication relation representation module to capture the inherent relation between drugs based on the patient’s condition. Extensive experiments on the MIMIC-III dataset demonstrate that our approach achieves new state-of-the-art performance1.