{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T18:59:36Z","timestamp":1776365976752,"version":"3.51.2"},"reference-count":32,"publisher":"Wiley","issue":"22","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100002655","name":"Beckman Coulter Foundation","doi-asserted-by":"publisher","award":["1100 euros"],"award-info":[{"award-number":["1100 euros"]}],"id":[{"id":"10.13039\/100002655","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Cellular Molecular Medi"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Monocytes and neutrophils play key roles in the cytokine storm triggered by SARS\u2010CoV\u20102 infection, which changes their conformation and function. These changes are detectable at the cellular and molecular level and may be different to what is observed in other respiratory infections. Here, we applied machine learning (ML) to develop and validate an algorithm to diagnose COVID\u201019 using blood parameters. In this retrospective single\u2010center study, 49 hemogram parameters from 12,321 patients with clinical suspicion of COVID\u201019 and tested by RT\u2010PCR (4239 positive and 8082 negative) were analysed. The dataset was randomly divided into training and validation sets. Blood cell parameters and patient age were used to construct the predictive model with the support vector machine (SVM) tool. The model constructed from the training set (5936 patients) achieved an accuracy for diagnosis of SARS\u2010CoV\u20102 infection of 0.952 (95% CI: 0.875\u20130.892). Test sensitivity and specificity was 0.868 and 0.899, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.896 and 0.872, respectively (prevalence 0.50). The validation set model (4964 patients) achieved an accuracy of 0.894 (95% CI: 0.883\u20130.903). Test sensitivity and specificity was 0.8922 and 0.8951, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.817 and 0.94, respectively (prevalence 0.34). The area under the receiver operating characteristic curve was 0.952 for the algorithm performance. This algorithm may allow to rule out COVID\u201019 diagnosis with 94% of probability. This represents a great advance for early diagnostic orientation and guiding clinical decisions.<\/jats:p>","DOI":"10.1111\/jcmm.17864","type":"journal-article","created":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T12:18:35Z","timestamp":1698322715000},"page":"3423-3430","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A machine learning tool for the diagnosis of <scp>SARS\u2010CoV<\/scp>\u20102 infection from hemogram parameters"],"prefix":"10.1111","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6293-5210","authenticated-orcid":false,"given":"S.","family":"G\u00f3mez\u2010Rojas","sequence":"first","affiliation":[{"name":"Department of Hematology Hospital Universitario 12 octubre  Madrid Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1322-6065","authenticated-orcid":false,"given":"G. P\u00e9rez","family":"Segura","sequence":"additional","affiliation":[{"name":"Department of Hematology Hospital Universitario 12 octubre  Madrid Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.","family":"Oll\u00e9","sequence":"additional","affiliation":[{"name":"Conceptos Claros Co  Barcelona Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Carre\u00f1o\u00a0G\u00f3mez\u2010Tarragona","sequence":"additional","affiliation":[{"name":"Department of Hematology Hospital Universitario 12 octubre  Madrid Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. Gonz\u00e1lez","family":"Medina","sequence":"additional","affiliation":[{"name":"Department of Hematology Hospital Universitario Fundaci\u00f3n Jim\u00e9nez D\u00edaz  Madrid Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9520-8255","authenticated-orcid":false,"given":"J. M.","family":"Aguado","sequence":"additional","affiliation":[{"name":"Unit of Infectious Diseases Hospital Universitario \"12 de Octubre\", Instituto de Investigaci\u00f3n Sanitaria Hospital \"12 de Octubre\" (i+12), CIBERINFEC, ISCIII  Madrid Spain"},{"name":"Department of Medicine, School of Medicine Universidad Complutense  Madrid Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E. Vera","family":"Guerrero","sequence":"additional","affiliation":[{"name":"Department of Hematology Hospital Universitario 12 octubre  Madrid Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. 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