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Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms
In order to limit the spread of the novel betacoronavirus (SARS-CoV-2), it is necessary to detect positive cases as soon as possible and isolate them. For this purpose, machine-learning algorithms, as a field of artificial intelligence, have been recognized as a promising tool. The aim of this study...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383367/ https://www.ncbi.nlm.nih.gov/pubmed/37515208 http://dx.doi.org/10.3390/v15071522 |
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author | Dobrijević, Dejan Vilotijević-Dautović, Gordana Katanić, Jasmina Horvat, Mirjana Horvat, Zoltan Pastor, Kristian |
author_facet | Dobrijević, Dejan Vilotijević-Dautović, Gordana Katanić, Jasmina Horvat, Mirjana Horvat, Zoltan Pastor, Kristian |
author_sort | Dobrijević, Dejan |
collection | PubMed |
description | In order to limit the spread of the novel betacoronavirus (SARS-CoV-2), it is necessary to detect positive cases as soon as possible and isolate them. For this purpose, machine-learning algorithms, as a field of artificial intelligence, have been recognized as a promising tool. The aim of this study was to assess the utility of the most common machine-learning algorithms in the rapid triage of children with suspected COVID-19 using easily accessible and inexpensive laboratory parameters. A cross-sectional study was conducted on 566 children treated for respiratory diseases: 280 children with PCR-confirmed SARS-CoV-2 infection and 286 children with respiratory symptoms who were SARS-CoV-2 PCR-negative (control group). Six machine-learning algorithms, based on the blood laboratory data, were tested: random forest, support vector machine, linear discriminant analysis, artificial neural network, k-nearest neighbors, and decision tree. The training set was validated through stratified cross-validation, while the performance of each algorithm was confirmed by an independent test set. Random forest and support vector machine models demonstrated the highest accuracy of 85% and 82.1%, respectively. The models demonstrated better sensitivity than specificity and better negative predictive value than positive predictive value. The F1 score was higher for the random forest than for the support vector machine model, 85.2% and 82.3%, respectively. This study might have significant clinical applications, helping healthcare providers identify children with COVID-19 in the early stage, prior to PCR and/or antigen testing. Additionally, machine-learning algorithms could improve overall testing efficiency with no extra costs for the healthcare facility. |
format | Online Article Text |
id | pubmed-10383367 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103833672023-07-30 Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms Dobrijević, Dejan Vilotijević-Dautović, Gordana Katanić, Jasmina Horvat, Mirjana Horvat, Zoltan Pastor, Kristian Viruses Article In order to limit the spread of the novel betacoronavirus (SARS-CoV-2), it is necessary to detect positive cases as soon as possible and isolate them. For this purpose, machine-learning algorithms, as a field of artificial intelligence, have been recognized as a promising tool. The aim of this study was to assess the utility of the most common machine-learning algorithms in the rapid triage of children with suspected COVID-19 using easily accessible and inexpensive laboratory parameters. A cross-sectional study was conducted on 566 children treated for respiratory diseases: 280 children with PCR-confirmed SARS-CoV-2 infection and 286 children with respiratory symptoms who were SARS-CoV-2 PCR-negative (control group). Six machine-learning algorithms, based on the blood laboratory data, were tested: random forest, support vector machine, linear discriminant analysis, artificial neural network, k-nearest neighbors, and decision tree. The training set was validated through stratified cross-validation, while the performance of each algorithm was confirmed by an independent test set. Random forest and support vector machine models demonstrated the highest accuracy of 85% and 82.1%, respectively. The models demonstrated better sensitivity than specificity and better negative predictive value than positive predictive value. The F1 score was higher for the random forest than for the support vector machine model, 85.2% and 82.3%, respectively. This study might have significant clinical applications, helping healthcare providers identify children with COVID-19 in the early stage, prior to PCR and/or antigen testing. Additionally, machine-learning algorithms could improve overall testing efficiency with no extra costs for the healthcare facility. MDPI 2023-07-08 /pmc/articles/PMC10383367/ /pubmed/37515208 http://dx.doi.org/10.3390/v15071522 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dobrijević, Dejan Vilotijević-Dautović, Gordana Katanić, Jasmina Horvat, Mirjana Horvat, Zoltan Pastor, Kristian Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms |
title | Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms |
title_full | Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms |
title_fullStr | Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms |
title_full_unstemmed | Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms |
title_short | Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms |
title_sort | rapid triage of children with suspected covid-19 using laboratory-based machine-learning algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383367/ https://www.ncbi.nlm.nih.gov/pubmed/37515208 http://dx.doi.org/10.3390/v15071522 |
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