COVID-19 antibody level analysis with feature selection approach

The study presented here considers the analysis of a medical dataset for the identification of the stage of onset of COVID-19 coronavirus. These data, presented in previous work by the authors, have been subjected to extensive analysis and additional calculations. The data were obtained by analyzing...

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Autores principales: Paja, Wiesław, Pancerz, Krzysztof, Stoean, Catalin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9578923/
https://www.ncbi.nlm.nih.gov/pubmed/36275372
http://dx.doi.org/10.1016/j.procs.2022.09.490
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author Paja, Wiesław
Pancerz, Krzysztof
Stoean, Catalin
author_facet Paja, Wiesław
Pancerz, Krzysztof
Stoean, Catalin
author_sort Paja, Wiesław
collection PubMed
description The study presented here considers the analysis of a medical dataset for the identification of the stage of onset of COVID-19 coronavirus. These data, presented in previous work by the authors, have been subjected to extensive analysis and additional calculations. The data were obtained by analyzing blood samples of infected individuals at 1, 3, and 6 months after COVID-19 infection. Results were obtained from FTIR spectrometry experiments. The results indicate a very effective ability to identify the different states of infection, and between 1 and 6 months even perfect. Specific spectrometry wavelength ranges can also be distinguished as medical markers.
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spelling pubmed-95789232022-10-19 COVID-19 antibody level analysis with feature selection approach Paja, Wiesław Pancerz, Krzysztof Stoean, Catalin Procedia Comput Sci Article The study presented here considers the analysis of a medical dataset for the identification of the stage of onset of COVID-19 coronavirus. These data, presented in previous work by the authors, have been subjected to extensive analysis and additional calculations. The data were obtained by analyzing blood samples of infected individuals at 1, 3, and 6 months after COVID-19 infection. Results were obtained from FTIR spectrometry experiments. The results indicate a very effective ability to identify the different states of infection, and between 1 and 6 months even perfect. Specific spectrometry wavelength ranges can also be distinguished as medical markers. The Author(s). Published by Elsevier B.V. 2022 2022-10-19 /pmc/articles/PMC9578923/ /pubmed/36275372 http://dx.doi.org/10.1016/j.procs.2022.09.490 Text en © 2022 The Author(s). Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Paja, Wiesław
Pancerz, Krzysztof
Stoean, Catalin
COVID-19 antibody level analysis with feature selection approach
title COVID-19 antibody level analysis with feature selection approach
title_full COVID-19 antibody level analysis with feature selection approach
title_fullStr COVID-19 antibody level analysis with feature selection approach
title_full_unstemmed COVID-19 antibody level analysis with feature selection approach
title_short COVID-19 antibody level analysis with feature selection approach
title_sort covid-19 antibody level analysis with feature selection approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9578923/
https://www.ncbi.nlm.nih.gov/pubmed/36275372
http://dx.doi.org/10.1016/j.procs.2022.09.490
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