Predicting COVID-19 Cases Among Nurses Using Artificial Neural Network Approach.
Journal
Computers, informatics, nursing : CIN
ISSN: 1538-9774
Titre abrégé: Comput Inform Nurs
Pays: United States
ID NLM: 101141667
Informations de publication
Date de publication:
01 May 2022
01 May 2022
Historique:
pubmed:
27
4
2022
medline:
11
5
2022
entrez:
26
4
2022
Statut:
epublish
Résumé
We designed a forecasting model to determine which frontline health workers are most likely to be infected by COVID-19 among 220 nurses. We used multivariate regression analysis and different classification algorithms to assess the effect of several covariates, including exposure to COVID-19 patients, access to personal protective equipment, proper use of personal protective equipment, adherence to hand hygiene principles, stressfulness, and training on the risk of a nurse being infected. Access to personal protective equipment and training were associated with a 0.19- and 1.66-point lower score in being infected by COVID-19. Exposure to COVID-19 cases and being stressed of COVID-19 infection were associated with a 0.016- and 9.3-point higher probability of being infected by COVID-19. Furthermore, an artificial neural network with 75.8% (95% confidence interval, 72.1-78.9) validation accuracy and 76.6% (95% confidence interval, 73.1-78.6) overall accuracy could classify normal and infected nurses. The neural network can help managers and policymakers determine which frontline health workers are most likely to be infected by COVID-19.
Identifiants
pubmed: 35470304
doi: 10.1097/CIN.0000000000000907
pii: 00024665-202205000-00009
pmc: PMC9093222
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
341-349Informations de copyright
Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.
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