AIMC Topic: Adverse Drug Reaction Reporting Systems
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tcTKB: an integrated cardiovascular toxicity knowledge base for targeted cancer drugs.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Nov 5, 2015
Targeted cancer drugs are often associated with unexpectedly high cardiovascular (CV) adverse events. Systematic approaches to studying CV events associated with targeted anticancer drugs have high potential for elucidating the complex pathways under...
On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions.
Journal of biomedical informatics
Jun 30, 2015
The advances achieved in Natural Language Processing make it possible to automatically mine information from electronically created documents. Many Natural Language Processing methods that extract information from texts make use of annotated corpora,...
Algorithms
Electronic Health Records
Adverse Drug Reaction Reporting Systems
Language
Reproducibility of Results
Linguistics
Machine Learning
Predictive Value of Tests
Translating
Data Mining
Natural Language Processing
Pharmaceutical Preparations
Pharmacovigilance
Automation
Drug-Related Side Effects and Adverse Reactions
Toward a complete dataset of drug-drug interaction information from publicly available sources.
Journal of biomedical informatics
Apr 24, 2015
Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information ...
Effect of reporting bias in the analysis of spontaneous reporting data.
Pharmaceutical statistics
Nov 6, 2014
It is well-known that a spontaneous reporting system suffers from significant under-reporting of adverse drug reactions from the source population. The existing methods do not adjust for such under-reporting for the calculation of measures of associa...
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...
Detecting Adverse Drug Events in Clinical Notes Using Large Language Models.
Studies in health technology and informatics
May 15, 2025
Monitoring adverse drug events (ADEs) is critical for pharmacovigilance and patient safety. However, identifying ADEs remains challenging, as suspected or confirmed side effects are often documented solely in the unstructured text of electronic healt...
Leveraging Large Language Models for Synthetic Data Generation to Enhance Adverse Drug Event Detection in Tweets.
Studies in health technology and informatics
May 15, 2025
Adverse drug event (ADE) detection in social media texts poses significant challenges due to the informal nature of the text and the limited availability of annotations. The scarcity of ADE named entity recognition (NER) datasets for social media hin...
Leveraging Generative AI for Drug Safety and Pharmacovigilance.
Current reviews in clinical and experimental pharmacology
Jan 1, 2025
Predictions are made by artificial intelligence, especially through machine learning, which uses algorithms and past knowledge. Notably, there has been an increase in interest in using artificial intelligence, particularly generative AI, in the pharm...
Machine Learning upon RDF Knowledge Graphs for Drug Safety: A Case Study on Reactome Data.
Studies in health technology and informatics
Aug 22, 2024
Artificial Intelligence (AI), particularly Machine Learning (ML), has gained attention for its potential in various domains. However, approaches integrating symbolic AI with ML on Knowledge Graphs have not gained significant focus yet. We argue that ...
Causal Deep Learning for the Detection of Adverse Drug Reactions: Drug-Induced Acute Kidney Injury as a Case Study.
Studies in health technology and informatics
Aug 22, 2024
Causal Deep/Machine Learning (CDL/CML) is an emerging Artificial Intelligence (AI) paradigm. The combination of causal inference and AI could mine explainable causal relationships between data features, providing useful insights for various applicati...
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