Articles producció científica> Enginyeria Química

A machine learning COVID-19 mass screening based on symptoms and a simple olfactory test

  • Identification data

    Identifier: imarina:9282212
    Authors:
    Azeli, YoucefFernandez, AlbertoCapriles, FedericoRojewski, WojciechLopez-Madrid, VanesaSabate-Lissner, DavidSerrano, Rosa MariaRey-Renones, CristinaCivit, MartaCasellas, JosefinaEl Ouahabi-El Ouahabi, AbdelghaniFoglia-Fernandez, MariaSarra, SalvadorLlobet, Eduard
    Abstract:
    The early detection of symptoms and rapid testing are the basis of an efficient screening strategy to control COVID-19 transmission. The olfactory dysfunction is one of the most prevalent symptom and in many cases is the first symptom. This study aims to develop a machine learning COVID-19 predictive tool based on symptoms and a simple olfactory test, which consists of identifying the smell of an aromatized hydroalcoholic gel. A multi-centre population-based prospective study was carried out in the city of Reus (Catalonia, Spain). The study included consecutive patients undergoing a reverse transcriptase polymerase chain reaction test for presenting symptoms suggestive of COVID-19 or for being close contacts of a confirmed COVID-19 case. A total of 519 patients were included, 386 (74.4%) had at least one symptom and 133 (25.6%) were asymptomatic. A classification tree model including sex, age, relevant symptoms and the olfactory test results obtained a sensitivity of 0.97 (95% CI 0.91–0.99), a specificity of 0.39 (95% CI 0.34–0.44) and an AUC of 0.87 (95% CI 0.83–0.92). This shows that this machine learning predictive model is a promising mass screening for COVID-19.
  • Others:

    Author, as appears in the article.: Azeli, Youcef; Fernandez, Alberto; Capriles, Federico; Rojewski, Wojciech; Lopez-Madrid, Vanesa; Sabate-Lissner, David; Serrano, Rosa Maria; Rey-Renones, Cristina; Civit, Marta; Casellas, Josefina; El Ouahabi-El Ouahabi, Abdelghani; Foglia-Fernandez, Maria; Sarra, Salvador; Llobet, Eduard
    Department: Enginyeria Electrònica, Elèctrica i Automàtica Enginyeria Química
    URV's Author/s: Fernández Sabater, Alberto / Llobet Valero, Eduard
    Keywords: System
    Abstract: The early detection of symptoms and rapid testing are the basis of an efficient screening strategy to control COVID-19 transmission. The olfactory dysfunction is one of the most prevalent symptom and in many cases is the first symptom. This study aims to develop a machine learning COVID-19 predictive tool based on symptoms and a simple olfactory test, which consists of identifying the smell of an aromatized hydroalcoholic gel. A multi-centre population-based prospective study was carried out in the city of Reus (Catalonia, Spain). The study included consecutive patients undergoing a reverse transcriptase polymerase chain reaction test for presenting symptoms suggestive of COVID-19 or for being close contacts of a confirmed COVID-19 case. A total of 519 patients were included, 386 (74.4%) had at least one symptom and 133 (25.6%) were asymptomatic. A classification tree model including sex, age, relevant symptoms and the olfactory test results obtained a sensitivity of 0.97 (95% CI 0.91–0.99), a specificity of 0.39 (95% CI 0.34–0.44) and an AUC of 0.87 (95% CI 0.83–0.92). This shows that this machine learning predictive model is a promising mass screening for COVID-19.
    Thematic Areas: Zootecnia / recursos pesqueiros Saúde coletiva Química Psicología Odontología Nutrição Multidisciplinary sciences Multidisciplinary Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Letras / linguística Interdisciplinar Geografía Geociências Farmacia Engenharias iv Engenharias iii Engenharias ii Enfermagem Educação física Educação Economia Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Biotecnología Biodiversidade Astronomia / física
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: alberto.fernandez@urv.cat eduard.llobet@urv.cat
    Author identifier: 0000-0002-1241-1646 0000-0001-6164-4342
    Record's date: 2024-10-12
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.nature.com/articles/s41598-022-19817-x
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Scientific Reports. 12 (1): 15622-
    APA: Azeli, Youcef; Fernandez, Alberto; Capriles, Federico; Rojewski, Wojciech; Lopez-Madrid, Vanesa; Sabate-Lissner, David; Serrano, Rosa Maria; Rey-Renon (2022). A machine learning COVID-19 mass screening based on symptoms and a simple olfactory test. Scientific Reports, 12(1), 15622-. DOI: 10.1038/s41598-022-19817-x
    Article's DOI: 10.1038/s41598-022-19817-x
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Journal Publications
  • Keywords:

    Multidisciplinary,Multidisciplinary Sciences
    System
    Zootecnia / recursos pesqueiros
    Saúde coletiva
    Química
    Psicología
    Odontología
    Nutrição
    Multidisciplinary sciences
    Multidisciplinary
    Medicina veterinaria
    Medicina iii
    Medicina ii
    Medicina i
    Materiais
    Matemática / probabilidade e estatística
    Letras / linguística
    Interdisciplinar
    Geografía
    Geociências
    Farmacia
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Enfermagem
    Educação física
    Educação
    Economia
    Ciências biológicas iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência de alimentos
    Ciência da computação
    Biotecnología
    Biodiversidade
    Astronomia / física
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