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Predictive models for COVID-19 detection using routine blood tests and machine learning

Yury V. Kistenev, Denis A. Vrazhnov, Ekaterina E. Shnaider, Hala Zuhayri

2022Heliyon33 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

Coronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Computer science2019-20 coronavirus outbreakMachine learningSoftwareArtificial intelligenceMedical physicsMedicineVirologyPathologyInfectious disease (medical specialty)DiseaseOperating systemOutbreakCOVID-19 diagnosis using AICOVID-19 Clinical Research StudiesSARS-CoV-2 detection and testing
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