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

arXiv: https://arxiv.org/abs/2405.15766 · HTML: https://mlsanity.com/paper/2405.15766/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [GIT: A Generative Image-to-text Transformer for Vision and Language](https://mlsanity.com/paper/2205.14100/) (2022)
- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://mlsanity.com/paper/1909.02622/) (2019)
- [A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Models](https://mlsanity.com/paper/2405.09589/) (2024)
- [CLIPSyntel: CLIP and LLM Synergy for Multimodal Question Summarization in Healthcare](https://mlsanity.com/paper/2312.11541/) (2023)
- [MedSumm: A Multimodal Approach to Summarizing Code-Mixed Hindi-English Clinical Queries](https://mlsanity.com/paper/2401.01596/) (2024)
- [BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance](https://mlsanity.com/paper/2305.13395/) (2023)
- [Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework](https://mlsanity.com/paper/2009.09509/) (2020)

## Built on by (most cited first, 3 of 3)

- [Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding](https://mlsanity.com/paper/2505.16652/) (2025)
- [Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies](https://mlsanity.com/paper/2503.07306/) (2025)
- [Applicability Condition Extraction for Therapeutic Drug-Disease Relations](https://mlsanity.com/paper/2606.14031/) (2026)

## Abstract (arXiv)

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.