| アブストラクト | INTRODUCTION: The Vaccine Adverse Event Reporting System (VAERS) has already been challenged by an extreme increase in the number of individual case safety reports (ICSRs) after the market introduction of coronavirus disease 2019 (COVID-19) vaccines. Evidence from scientific literature suggests that when there is an extreme increase in the number of ICSRs recorded in spontaneous reporting databases (such as the VAERS), an accompanying increase in the number of disproportionality signals (sometimes referred to as 'statistical alerts') generated is expected. OBJECTIVES: The objective of this study was to develop a natural language processing (NLP)-based approach to optimize signal management by excluding disproportionality signals related to listed adverse events following immunization (AEFIs). COVID-19 vaccines were used as a proof-of-concept. METHODS: The VAERS was used as a data source, and the Finding Associated Concepts with Text Analysis (FACTA+) was used to extract signs and symptoms of listed AEFIs from MEDLINE for COVID-19 vaccines. Disproportionality analyses were conducted according to guidelines and recommendations provided by the US Centers for Disease Control and Prevention. By using signs and symptoms of listed AEFIs, we computed the proportion of disproportionality signals dismissed for COVID-19 vaccines using this approach. Nine NLP techniques, including Generative Pre-Trained Transformer 3.5 (GPT-3.5), were used to automatically retrieve Medical Dictionary for Regulatory Activities Preferred Terms (MedDRA PTs) from signs and symptoms extracted from FACTA+. RESULTS: Overall, 17% of disproportionality signals for COVID-19 vaccines were dismissed as they reported signs and symptoms of listed AEFIs. Eight of nine NLP techniques used to automatically retrieve MedDRA PTs from signs and symptoms extracted from FACTA+ showed suboptimal performance. GPT-3.5 achieved an accuracy of 78% in correctly assigning MedDRA PTs. CONCLUSION: Our approach reduced the need for manual exclusion of disproportionality signals related to listed AEFIs and may lead to better optimization of time and resources in signal management. |
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| 投稿者 | Dong, Guojun; Bate, Andrew; Haguinet, Francois; Westman, Gabriel; Durlich, Luise; Hviid, Anders; Sessa, Maurizio |
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| 組織名 | Department of Drug Design and Pharmacology, University of Copenhagen, Jagtvej;160, 2100, Copenhagen, Denmark.;Global Safety, GSK, Brentford, UK.;Department of Non‑Communicable Disease Epidemiology, London School of Hygiene and;Tropical Medicine, London, UK.;Department of Medical Sciences, Uppsala University, Uppsala, Sweden.;Department of Linguistics and Philology, Uppsala University, Uppsala, Sweden.;Department of Computer Science, RISE Research Institutes of Sweden, Kista,;Sweden.;Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark.;160, 2100, Copenhagen, Denmark. maurizio.sessa@sund.ku.dk. |
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