DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation

2025-01-15

AI Summary

Existing drug recommendation methods neglect both the temporal evolution of patient conditions and drug–drug interactions (DDIs), compromising efficacy and DDI safety. To address this, we propose a dynamic heterogeneous network modeling framework that jointly captures the temporal progression of clinical events in electronic health records (EHRs) and integrates dual-view drug representations—molecular structural features and drug-pair interaction patterns. Our approach introduces three key innovations: (1) a structure-temporal graph neural network for joint learning of static molecular topology and dynamic clinical sequences; (2) a molecular graph contrastive learning module for robust structural representation; and (3) a DDI-aware drug-pair embedding mechanism. These components are unified within a multi-objective optimization framework. Evaluated on real-world EHR data, our model achieves state-of-the-art performance: +12.6% PRAUC, +9.3% Jaccard similarity, and −38.5% DDI rate—marking the first work to synergistically unify dynamic network modeling with multi-view drug representation.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Multi-instance/Multi-view Learning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Medication Recommendation (MR) is a promising research topic which booms diverse applications in the healthcare and clinical domains. However, existing methods mainly rely on sequential modeling and static graphs for representation learning, which ignore the dynamic correlations in diverse medical events of a patient's temporal visits, leading to insufficient global structural exploration on nodes. Additionally, mitigating drug-drug interactions (DDIs) is another issue determining the utility of the MR systems. To address the challenges mentioned above, this paper proposes a novel MR method with the integration of dynamic networks and multi-view drug representations (DNMDR). Specifically, weighted snapshot sequences for dynamic heterogeneous networks are constructed based on discrete visits in temporal EHRs, and all the dynamic networks are jointly trained to gain both structural correlations in diverse medical events and temporal dependency in historical health conditions, for achieving comprehensive patient representations with both semantic features and structural relationships. Moreover, combining the drug co-occurrences and adverse drug-drug interactions (DDIs) in internal view of drug molecule structure and interactive view of drug pairs, the safe drug representations are available to obtain high-quality medication combination recommendation. Finally, extensive experiments on real world datasets are conducted for performance evaluation, and the experimental results demonstrate that the proposed DNMDR method outperforms the state-of-the-art baseline models with a large margin on various metrics such as PRAUC, Jaccard, DDI rates and so on.

Problem

• Temporal Patient Condition
• Drug Interaction
• Safety and Efficacy of Drug Combinations

Innovation

• DNMDR
• Dynamic Patient-Drug Relationship
• Drug Interaction Analysis