COVID-19 diagnosis by routine blood tests using machine learning

Abstract Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from performing COVID-19 diagnoses. We constructed a machine learning model for COVID-19 diagnosis that was based and cross-validated on the rout...

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Autores principales:	Matjaž Kukar, Gregor Gunčar, Tomaž Vovko, Simon Podnar, Peter Černelč, Miran Brvar, Mateja Zalaznik, Mateja Notar, Sašo Moškon, Marko Notar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5e6c746d9f7d42ca988860072ce91764
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spelling	oai:doaj.org-article:5e6c746d9f7d42ca988860072ce917642021-12-02T14:47:31ZCOVID-19 diagnosis by routine blood tests using machine learning10.1038/s41598-021-90265-92045-2322https://doaj.org/article/5e6c746d9f7d42ca988860072ce917642021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90265-9https://doaj.org/toc/2045-2322Abstract Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from performing COVID-19 diagnoses. We constructed a machine learning model for COVID-19 diagnosis that was based and cross-validated on the routine blood tests of 5333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected the operational ROC point at a sensitivity of 81.9% and a specificity of 97.9%. The cross-validated AUC was 0.97. The five most useful routine blood parameters for COVID-19 diagnosis according to the feature importance scoring of the XGBoost algorithm were: MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. t-SNE visualization showed that the blood parameters of the patients with a severe COVID-19 course are more like the parameters of a bacterial than a viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results represent a significant contribution to improvements in COVID-19 diagnosis.Matjaž KukarGregor GunčarTomaž VovkoSimon PodnarPeter ČernelčMiran BrvarMateja ZalaznikMateja NotarSašo MoškonMarko NotarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matjaž Kukar Gregor Gunčar Tomaž Vovko Simon Podnar Peter Černelč Miran Brvar Mateja Zalaznik Mateja Notar Sašo Moškon Marko Notar COVID-19 diagnosis by routine blood tests using machine learning
description	Abstract Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from performing COVID-19 diagnoses. We constructed a machine learning model for COVID-19 diagnosis that was based and cross-validated on the routine blood tests of 5333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected the operational ROC point at a sensitivity of 81.9% and a specificity of 97.9%. The cross-validated AUC was 0.97. The five most useful routine blood parameters for COVID-19 diagnosis according to the feature importance scoring of the XGBoost algorithm were: MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. t-SNE visualization showed that the blood parameters of the patients with a severe COVID-19 course are more like the parameters of a bacterial than a viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results represent a significant contribution to improvements in COVID-19 diagnosis.
format	article
author	Matjaž Kukar Gregor Gunčar Tomaž Vovko Simon Podnar Peter Černelč Miran Brvar Mateja Zalaznik Mateja Notar Sašo Moškon Marko Notar
author_facet	Matjaž Kukar Gregor Gunčar Tomaž Vovko Simon Podnar Peter Černelč Miran Brvar Mateja Zalaznik Mateja Notar Sašo Moškon Marko Notar
author_sort	Matjaž Kukar
title	COVID-19 diagnosis by routine blood tests using machine learning
title_short	COVID-19 diagnosis by routine blood tests using machine learning
title_full	COVID-19 diagnosis by routine blood tests using machine learning
title_fullStr	COVID-19 diagnosis by routine blood tests using machine learning
title_full_unstemmed	COVID-19 diagnosis by routine blood tests using machine learning
title_sort	covid-19 diagnosis by routine blood tests using machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5e6c746d9f7d42ca988860072ce91764
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COVID-19 diagnosis by routine blood tests using machine learning

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