Assessing adverse event reports of hysteroscopic sterilization device removal using natural language processing.
female sterilization
medical device safety
research methods
Journal
Pharmacoepidemiology and drug safety
ISSN: 1099-1557
Titre abrégé: Pharmacoepidemiol Drug Saf
Pays: England
ID NLM: 9208369
Informations de publication
Date de publication:
04 2022
04 2022
Historique:
revised:
09
12
2021
received:
16
03
2021
accepted:
13
12
2021
pubmed:
18
12
2021
medline:
26
3
2022
entrez:
17
12
2021
Statut:
ppublish
Résumé
To develop an annotation model to apply natural language processing (NLP) to device adverse event reports and implement the model to evaluate the most frequently experienced events among women reporting a sterilization device removal. We included adverse event reports from the Manufacturer and User Facility Device Experience database from January 2005 to June 2018 related to device removal following hysteroscopic sterilization. We used an iterative process to develop an annotation model that extracts six categories of desired information and applied the annotation model to train an NLP algorithm. We assessed the model performance using positive predictive value (PPV, also known as precision), sensitivity (also known as recall), and F The overall F We present a roadmap to develop an annotation model for NLP to analyze device adverse event reports. The extracted information is informative and complements findings from previous research using administrative data.
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
442-451Subventions
Organisme : AHRQ HHS
ID : R03 HS026291
Pays : United States
Organisme : FDA HHS
ID : U01 FD005478
Pays : United States
Informations de copyright
© 2021 John Wiley & Sons Ltd.
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