Summary of Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development, by Pranab Sahoo et al.
Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development
by Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Aman Chadha, Samrat Mondal
First submitted to arxiv on: 24 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to detecting adverse drug events (ADEs) by combining text-based methodologies with visual cues from medical images. The proposed MultiModal Adverse Drug Event (MMADE) detection dataset integrates ADE-related textual information with visual aids, enabling more accurate and comprehensive ADE detection. The framework leverages the capabilities of Large Language Models (LLMs) and Vision-Language Models (VLMs) to generate detailed descriptions of medical images depicting ADEs, aiding healthcare professionals in visually identifying adverse events. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve patient safety by developing a new method for detecting adverse drug events. It combines text-based information with visual cues from medical images to identify potential risks associated with medications. The approach uses machine learning algorithms and large datasets to analyze unstructured texts, social media content, biomedical literature, and Electronic Medical Records (EMR). This could lead to earlier detection of adverse events and better decision-making for healthcare professionals. |
Keywords
» Artificial intelligence » Machine learning
