Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Optimizing Signal Management in a Vaccine Adverse Event Reporting System: A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing
University of Copenhagen, Denmark.
GSK, United Kingdom; London School of Hygiene and Tropical Medicine, United Kingdom.
GSK, United Kingdom.
Uppsala University, Sweden.
Vise andre og tillknytning
2024 (engelsk)Inngår i: Drug Safety, ISSN 0114-5916, E-ISSN 1179-1942, Vol. 47, nr 2, s. 173-Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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. © 2023, The Author(s).

sted, utgiver, år, opplag, sider
Adis , 2024. Vol. 47, nr 2, s. 173-
HSV kategori
Identifikatorer
URN: urn:nbn:se:ri:diva-68786DOI: 10.1007/s40264-023-01381-6Scopus ID: 2-s2.0-85178895864OAI: oai:DiVA.org:ri-68786DiVA, id: diva2:1827556
Tilgjengelig fra: 2024-01-15 Laget: 2024-01-15 Sist oppdatert: 2024-05-27

Open Access i DiVA

fulltext(922 kB)38 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 922 kBChecksum SHA-512
941cdac8176aef2df4ae68e730f9ea615efbd62d44e9bc8ecb2a8bf12e53f35d670dcff4473a7ecc43de07597e878df0a6552b8fdcbb86c29a032a1d067d0621
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekstScopus

Person

Dürlich, Luise

Søk i DiVA

Av forfatter/redaktør
Dürlich, Luise
Av organisasjonen
I samme tidsskrift
Drug Safety

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 38 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 172 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
v. 2.43.0