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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Detalles Bibliográficos
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
Descripción
Sumario: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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