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

TL;DR

This paper introduces DNMDR, a novel medication recommendation method that leverages dynamic networks and multi-view drug representations to improve safety and effectiveness by capturing temporal medical event correlations and drug interactions.

Contribution

The paper proposes a dynamic network-based approach with multi-view drug representations to enhance medication recommendation accuracy and safety, addressing limitations of static models.

Findings

Outperforms state-of-the-art models on PRAUC, Jaccard, and DDI metrics.

Effectively captures temporal correlations in patient health data.

Reduces drug-drug interaction rates in recommendations.

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.