Leveraging Established Machine Learning Methodology to Tackle Viral Infections in Emergency Departments: A Retrospective Analysis using Hemocytometric Data to detect SARS-CoV-2

Leveraging Established Machine Learning Methodology to Tackle Viral Infections in Emergency Departments: A Retrospective Analysis using Hemocytometric Data to detect SARS-CoV-2 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Leveraging Established Machine Learning Methodology to Tackle Viral Infections in Emergency Departments: A Retrospective Analysis using Hemocytometric Data to detect SARS-CoV-2 R. Lyana CURIER, Calvin Brouwer, Hilda wardak, Daan van Twist, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3181908/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract During the COVID-19 pandemic, the healthcare system faced significant strain. This study aimed to achieve two objectives: developing a machine learning model using only haemocytometric data to accurately identify COVID-19 positive patients in Emergency Departments (EDs) and empowering non-technical healthcare professionals to assess model performance and make informed decisions about implementing machine learning models in healthcare settings. This study utilized data from suspected COVID-19 admissions at Zuyderland Medical Center and employed principal component analysis, agglomerative hierarchical clustering, followed by Random Forest to build and evaluate the prediction model. Model validation was conducted using a separate dataset. The resulting model, based on 14 haemocytometric parameters, achieved an overall weighted accuracy of 0.88, with specificity, sensitivity, positive predictive values, and negative predictive values of 0.93, 0.78, 0.86, and 0.89, respectively. Concentrations of highly fluorescent lymphocytes, basophils, and eosinophils had the most significant impact on the model's output. This study demonstrates the potential of a well-trained machine learning model to predict COVID-19 infection within one hour using easily accessible laboratory data. Implementing this model in Emergency Departments could provide valuable support and alleviate staff workload. Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Computational biology and bioinformatics/Predictive medicine Health sciences/Health care/Public health/Population screening Biological sciences/Microbiology/Infectious disease diagnostics Full Text Additional Declarations No competing interests reported. Supplementary Files fwavestatinfoSupplmat1.csv Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3181908","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":221126265,"identity":"0b21a56c-d198-499b-9630-452b3198878b","order_by":0,"name":"R. 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