Intelligent Medication Recommendation via Dynamic Prescription Modeling and Molecular Substructure Learning.

MoTRANet: Safety-Aware Drug Recommendation via Molecular Representation Learning and Dynamic Patient Modeling.

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.

MGRec: Structure-Grounded Medication Recommendation via Condition-Aware Molecular Representation Learning

Medication recommendation plays a critical role in clinical decision-making by supporting personalized and safe treatment planning. Existing methods rely heavily on historical co-occurrence patterns and primarily optimize discrete prescription prediction objectives, limiting generalization in rare or emerging disease s...

Modeling Complex Short EHR Visits With xLSTM and Medical Entity Enhancement for Medication Recommendation

Medication recommendation aims to provide accurate and safe medication combinations tailored to patients’ clinical records. Existing methods suffer from two key limitations: insufficient modeling of disease–medication relationships, resulting in under-expressive medical entity embeddings, and limited capability in capt...

Disease Spectrum-aware and Time-evolving Dependency Learning for Medication Recommendation

Precision pharmacology: deep learning infused ontological framework with E-GRU enhancement for tailored medicine prescriptions

The NexusOpti model, incorporating the Enhanced Gated Recurrent Unit (E-GRU) layer, outperforms the existing GRU model in terms of NDCG and Hit Ratio metrics and highlights the effectiveness of the model in advancing personalised, safer, and data-driven medication prescriptions.

MKMed: Multi-Knowledge alignment framework for medication recommendation