AIMC Topic: Adverse Drug Reaction Reporting Systems

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The need for guardrails with large language models in pharmacovigilance and other medical safety critical settings.

Scientific reports Jul 31, 2025
Large language models (LLMs) are useful tools with the capacity for performing specific types of knowledge work at an effective scale. However, LLM deployments in high-risk and safety-critical domains pose unique challenges, notably the issue of "hal...
Humans Patient Safety Pharmacovigilance Adverse Drug Reaction Reporting Systems Large Language Models Language Natural Language Processing Drug-Related Side Effects and Adverse Reactions
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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...
Gout Aged Humans Middle Aged United States Food and Drug Administration Adult Female United States Adverse Drug Reaction Reporting Systems Male Risk Factors Hyperuricemia
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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...
Social Media Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Humans Machine Learning Adverse Drug Reaction Reporting Systems
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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...
Adverse Drug Reaction Reporting Systems Humans Artificial Intelligence Machine Learning Natural Language Processing Drug-Related Side Effects and Adverse Reactions Pharmacovigilance
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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...
Databases, Factual Narration Drug-Related Side Effects and Adverse Reactions Humans Artificial Intelligence Adverse Drug Reaction Reporting Systems Search Engine
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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...
Singapore Patient Discharge Drug-Related Side Effects and Adverse Reactions Patient Discharge Summaries Adverse Drug Reaction Reporting Systems Humans Machine Learning Large Language Models Databases, Factual Natural Language Processing
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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 ...
Humans Deep Learning Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems Machine Learning Algorithms
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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 ...
Myocardial Infarction Machine Learning Drug-Related Side Effects and Adverse Reactions Databases, Factual Humans Cross-Over Studies Electronic Health Records Proof of Concept Study Adverse Drug Reaction Reporting Systems Male Female Middle Aged Aged Italy
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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...
Social Media Humans Natural Language Processing COVID-19 Adverse Drug Reaction Reporting Systems SARS-CoV-2 Deep Learning Data Mining COVID-19 Vaccines Large Language Models
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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 ...
Electronic Health Records Humans Pharmacovigilance Natural Language Processing Machine Learning Adverse Drug Reaction Reporting Systems Drug-Related Side Effects and Adverse Reactions
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