Publications by authors named "G Noren"
Automated redaction of names in adverse event reports using transformer-based neural networks.
BMC Med Inform Decis Mak
December 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 clinical course of events, differential diagnosis, and patient-reported reflections may often only be conveyed in narrative form. The aim of this study is to develop and evaluate a method for automated redaction of person names in English narrative text on adverse event reports.
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- Disproportionality analyses in pharmacovigilance are essential for detecting safety signals from individual case safety reports, but there are no clear guidelines for publishing these analyses, leading to potential misunderstandings.
- The READUS-PV statement was created to address these concerns by providing a set of 14 guidelines aimed at improving the transparency and comprehensiveness of disproportionality studies.
- By promoting better reporting standards, the READUS-PV guidelines aim to enhance the accuracy of drug safety data interpretation and support evidence-based decision-making in pharmacovigilance.
Article Synopsis
- Disproportionality analyses are key for detecting drug safety signals but are often poorly reported, prompting the creation of new guidelines called READUS-PV for better reporting practices.
- A panel of 34 international experts developed these guidelines through surveys and consensus meetings, resulting in a checklist of 32 recommendations for reporting in main articles and 12 for abstracts.
- The implementation of READUS-PV aims to enhance transparency and improve the quality of publications related to drug safety signals.
Introduction And Objective: The European Medicines Agency (EMA) maintains a list of designated medical events (DMEs), events that are inherently serious and are prioritized for signal detection, irrespective of statistical criteria. We have analysed the results of our previously published scoping review to determine whether DME signals differ from those of other adverse events in terms of time to communication and characteristics of supporting reports of suspected adverse drug reactions.
Methods: For all signals, we obtained the launch year of medicinal products from textbooks or regulatory agencies, extracted the year of the first report in VigiBase and calculated the interval between the first report and communication (time to communication, TTC).
Introduction: Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made.
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