A multifaceted approach to drug-drug interaction extraction with fusion strategies.

Abstract

Drug-drug interactions (DDIs) occur when one medication affects the efficacy of another, potentially leading to unforeseen patient outcomes. Existing studies primarily focus on textual data, but overlook a wealth of the drug's multimodal information. This study aims to enhance DDI extraction by integrating diverse data modalities and evaluating various fusion strategies. We introduce a multimodal approach that integrates diverse representations of drug information (scientific text, graphs, formulas, images, and descriptions) to enhance the detection of drug-drug interactions. We explored various fusion techniques to effectively combine these modalities across early, intermediate, and late fusion phases. Additionally, we identify the factors contributing to failed cases, providing insights into the model's limitations and potential improvements. We have conducted experiments using publicly available DDI datasets, demonstrating significant improvements compared to existing methods. The proposed model significantly outperformed existing methods in DDI detection. Intermediate fusion strategies, particularly prediction-level concatenation, demonstrated superior accuracy and robustness. Detailed analyses identified factors contributing to failed cases, offering insights for future improvements. The findings highlight the potential of multimodal fusion to enhance predictive accuracy, providing a foundation for safer drug therapies and better-informed clinical decisions.