Sep 2026· Computer Methods in Biomechanics and Biomedical Engineering· pp.
1-17
· 0 citations· 27 references
Medicine
TL;DR
RTSANet is proposed, which integrates inter-visit drug similarity and external adverse drug reaction information, and captures medication relationships across visits, enhances drug representations with safety-related knowledge, and generates more accurate and safer medication combinations.
Abstract
Medication recommendation supports safe and personalized treatment based on patients' electronic health records(EHR). However, modeling complex dependencies in longitudinal records while ensuring medication safety remains challenging. We propose RTSANet, which integrates inter-visit drug similarity and external adverse drug reaction information. It captures medication relationships across visits, enhances drug representations with safety-related knowledge, and generates more accurate and safer medication combinations. Validation on the MIMIC-III and MIMIC-IV datasets demonstrates that RTSANet achieves 0.5360 Jaccard, 0.6895 F1 and 0.7869 PRAUC, improving recommendation accuracy and medication safety.
DynMedRec captures fine-grained drug information by modeling the interactions between substructures and employing a clinical-context query mechanism to generate adaptive molecular representations, and integrates structural correlations among medical codes to enhance visit-level patient representations.
Experimental results demonstrate that MoTRANet outperforms existing mainstream baseline methods in terms of Jaccard, F1, and PRAUC, while achieving a relatively low DDI rate, validating the model's accuracy and safety.
Peng-Tao Jia, Jun-Hao Gao, Jing-Tao Sha et al.· IEEE transactions on computa...· 0 citations
XMERec is proposed, a dual-level medication recommendation framework that jointly models medical entity interactions and short-term visit sequences and is fused with an association-guided correction mechanism to improve recommendation accuracy and safety.
Qing-Chuan Xu, K. Che, Jia-Feng Li et al.· IEEE Access· 0 citations
SpecTD-MR, a novel medication recommendation framework for Disease Spectrum-aware and Time-evolving Dependency Learning, is proposed that surpasses state-of-the-art baselines in accuracy, while providing visualizable structural associations to support insights into dynamic disease evolution.
Yi-Shuo Li, Cong Wang, Shou-Jin Wang et al.· ACM Transactions on Informat...· 1 citation
A novel framework named Multi-Knowledge Medication recommendation is proposed, which introduces a cross-modal medication encoder capable of aligning heterogeneous modalities into a unified representation space and explicitly mitigates the bucket effect, enabling robust recommendation performance even in sparse knowledg...
Hai-Xu Ma, Guan-Yong Wu, Shi-Zhuo Mu et al.· Applied intelligence (Boston...· 0 citations
MGRec is a framework that shifts learning from discrete prescription prediction to condition-aware modeling in a continuous molecular representation space and introduces a DDI (drug-drug interaction)-guided latent regularization to integrate drug interaction knowledge at the representation level to improve clinical saf...
Jin-Ke Feng, Wenjie Du· Proceedings of the Thirty-Fi...· 0 citations
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