Predictive models for COVID-19 detection using routine blood tests and machine learning

The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using...

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Autores principales: Kistenev, Yury V., Vrazhnov, Denis A., Shnaider, Ekaterina E., Zuhayri, Hala
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Review Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9595489/
https://www.ncbi.nlm.nih.gov/pubmed/36311357
http://dx.doi.org/10.1016/j.heliyon.2022.e11185
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author Kistenev, Yury V.
Vrazhnov, Denis A.
Shnaider, Ekaterina E.
Zuhayri, Hala
author_facet Kistenev, Yury V.
Vrazhnov, Denis A.
Shnaider, Ekaterina E.
Zuhayri, Hala
author_sort Kistenev, Yury V.
collection PubMed
description The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient’s state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning.
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spelling pubmed-95954892022-10-25 Predictive models for COVID-19 detection using routine blood tests and machine learning Kistenev, Yury V. Vrazhnov, Denis A. Shnaider, Ekaterina E. Zuhayri, Hala Heliyon Review Article The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient’s state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning. Elsevier 2022-10-22 /pmc/articles/PMC9595489/ /pubmed/36311357 http://dx.doi.org/10.1016/j.heliyon.2022.e11185 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Review Article
Kistenev, Yury V.
Vrazhnov, Denis A.
Shnaider, Ekaterina E.
Zuhayri, Hala
Predictive models for COVID-19 detection using routine blood tests and machine learning
title Predictive models for COVID-19 detection using routine blood tests and machine learning
title_full Predictive models for COVID-19 detection using routine blood tests and machine learning
title_fullStr Predictive models for COVID-19 detection using routine blood tests and machine learning
title_full_unstemmed Predictive models for COVID-19 detection using routine blood tests and machine learning
title_short Predictive models for COVID-19 detection using routine blood tests and machine learning
title_sort predictive models for covid-19 detection using routine blood tests and machine learning
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9595489/
https://www.ncbi.nlm.nih.gov/pubmed/36311357
http://dx.doi.org/10.1016/j.heliyon.2022.e11185
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