Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development

TL;DR

This paper introduces a multimodal dataset combining text and images for adverse drug event detection and develops a framework leveraging large language and vision models to improve identification accuracy.

Contribution

The study creates the MMADE dataset integrating textual and visual data for ADE detection and proposes a novel multimodal framework utilizing LLMs and VLMs for enhanced accuracy.

Findings

Integrating visual cues improves ADE detection performance.

The MMADE dataset enables multimodal ADE analysis.

The framework effectively combines text and images for better results.

Abstract

The mining of adverse drug events (ADEs) is pivotal in pharmacovigilance, enhancing patient safety by identifying potential risks associated with medications, facilitating early detection of adverse events, and guiding regulatory decision-making. Traditional ADE detection methods are reliable but slow, not easily adaptable to large-scale operations, and offer limited information. With the exponential increase in data sources like social media content, biomedical literature, and Electronic Medical Records (EMR), extracting relevant ADE-related information from these unstructured texts is imperative. Previous ADE mining studies have focused on text-based methodologies, overlooking visual cues, limiting contextual comprehension, and hindering accurate interpretation. To address this gap, we present a MultiModal Adverse Drug Event (MMADE) detection dataset, merging ADE-related textual information with visual aids. Additionally, we introduce a framework that leverages the capabilities of LLMs and VLMs for ADE detection by generating detailed descriptions of medical images depicting ADEs, aiding healthcare professionals in visually identifying adverse events. Using our MMADE dataset, we showcase the significance of integrating visual cues from images to enhance overall performance. This approach holds promise for patient safety, ADE awareness, and healthcare accessibility, paving the way for further exploration in personalized healthcare.