AIMC Topic: Adverse Drug Reaction Reporting Systems
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Assessment of drug induced hyperuricemia and gout risk using the FDA adverse event reporting system.
Scientific reports
Jul 2, 2025
Hyperuricemia, the key pathological basis of gout, is increasingly prevalent worldwide. While lifestyle factors contribute, various medications also play a role. However, their specific risks and mechanisms remain inadequately studied. Disproportiona...
Pharmacovigilance in the digital age: gaining insight from social media data.
Experimental biology and medicine (Maywood, N.J.)
May 27, 2025
Pharmacovigilance is essential for protecting patient health by monitoring and managing medication-related risks. Traditional methods like spontaneous reporting systems and clinical trials are valuable for identifying adverse drug events, but face de...
Artificial Intelligence: Applications in Pharmacovigilance Signal Management.
Pharmaceutical medicine
Apr 21, 2025
Pharmacovigilance is the science of collection, detection, and assessment of adverse events associated with pharmaceutical products for the ongoing monitoring and understanding of those products' safety profiles. Part of this process, signal manageme...
Narrative Search Engine for Case Series Assessment Supported by Artificial Intelligence Query Suggestions.
Drug safety
Mar 15, 2025
INTRODUCTION: Manual identification of case narratives with specific relevant information can be challenging when working with large numbers of adverse event reports (case series). The process can be supported with a search engine, but building searc...
Effectiveness of Transformer-Based Large Language Models in Identifying Adverse Drug Reaction Relations from Unstructured Discharge Summaries in Singapore.
Drug safety
Feb 21, 2025
INTRODUCTION: Transformer-based large language models (LLMs) have transformed the field of natural language processing and led to significant advancements in various text processing tasks. However, the applicability of these LLMs in identifying relat...
ADR-DQPU: A Novel ADR Signal Detection Using Deep Reinforcement and Positive-Unlabeled Learning.
IEEE journal of biomedical and health informatics
Feb 10, 2025
The medical community has grappled with the challenge of analysis and early detection of severe and unknown adverse drug reactions (ADRs) from Spontaneous Reporting Systems (SRSs) like the FDA Adverse Event Reporting System (FAERS), which often lack ...
Network Analysis and Machine Learning for Signal Detection and Prioritization Using Electronic Healthcare Records and Administrative Databases: A Proof of Concept in Drug-Induced Acute Myocardial Infarction.
Drug safety
Feb 7, 2025
BACKGROUND: Safety signals for potential drug-induced adverse events (AEs) typically emerge from multiple data sources, primarily spontaneous reporting systems, despite known limitations. Increasingly, real-world data from sources such as electronic ...
Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media.
Journal of biomedical informatics
Feb 7, 2025
OBJECTIVE: Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of these products. Traditional dee...
Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.
Drug safety
Jan 9, 2025
BACKGROUND: Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence ...
Automated redaction of names in adverse event reports using transformer-based neural networks.
BMC medical informatics and decision making
Dec 23, 2024
BACKGROUND: Automated recognition and redaction of personal identifiers in free text can enable organisations to share data while protecting privacy. This is important in the context of pharmacovigilance since relevant detailed information on the cli...
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