Predicting COVID-19 Cases Among Nurses Using Artificial Neural Network Approach.
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 patient...
| Publicado en: | CIN: Computers, Informatics, Nursing Vol. 40; no. 5; pp. 341 - 350 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
May2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156736425&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156736425 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15382931 KXN jtl: CIN: Computers, Informatics, Nursing issn: 15382931 maglogo: N pubinfo: dt: May2022 vid: 40 iid: 5 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 156736425 156736425 156736425 10.1097/CIN.0000000000000907 156736425 ppf: 341 ppct: 9 formats: tig: atl: Predicting COVID-19 Cases Among Nurses Using Artificial Neural Network Approach. aug: au: Namdar, Peyman Shafiekhani, Sajad Teymori, Fatemeh Abdollahzade, Sina Maleki, Aisa Rafiei, Sima affil: Author Affiliations: School of Medicine (Drs Namdar and Abdollahzade), Qazvin University of Medical Sciences (Ms Teymori) sug: subj: COVID-19 Risk Factors Risk Assessment Nursing Staff, Hospital Psychosocial Factors Neural Networks (Computer) Human Iran Artificial Intelligence Descriptive Research Analytic Research Questionnaires Data Analysis Software Descriptive Statistics Analysis of Variance Chi Square Test Regression Algorithms Confidence Intervals Male Female Personal Protective Equipment Occupational Exposure Adverse Effects Professional Compliance Handwashing Stress, Occupational Adverse Effects Probability Prediction Models Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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