Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development
CoRR(2024)
摘要
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.
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