dc.creator | Callejón-Leblic, María A. | es |
dc.creator | Moreno-Luna, Ramón | es |
dc.creator | Cuvillo, Alfonso del | es |
dc.creator | Reyes-Tejero, Isabel M. | es |
dc.creator | García-Villarán, Miguel Á. | es |
dc.creator | Santos-Peña, Marta | es |
dc.creator | Maza Solano, Juan Manuel | es |
dc.creator | Solanellas Soler, Juan | es |
dc.creator | Sánchez Gómez, Serafín | es |
dc.date.accessioned | 2022-09-23T15:15:21Z | |
dc.date.available | 2022-09-23T15:15:21Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Callejón-Leblic, M.A., Moreno-Luna, R., Cuvillo, A.d., Reyes-Tejero, I.M., García-Villarán, M.Á., Santos-Peña, M.,...,Sánchez Gómez, S. (2021). Loss of smell and taste can accurately predict COVID-19 infection: a machine-learning approach. Journal of Clinical Medicine, 10 (4) | |
dc.identifier.issn | 2077-0383 | es |
dc.identifier.uri | https://hdl.handle.net/11441/137345 | |
dc.description.abstract | The COVID-19 outbreak has spread extensively around the world. Loss of smell and
taste have emerged as main predictors for COVID-19. The objective of our study is to develop a
comprehensive machine learning (ML) modelling framework to assess the predictive value of smell
and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control
study was performed, in which suspected cases for COVID-19, who were tested by real-time reversetranscription
polymerase chain reaction (RT-PCR), informed about the presence and severity of their
symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to
predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the
patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of
smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19
positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and
a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that
smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for
COVID-19 diagnostic prediction. | es |
dc.description.sponsorship | Junta de Andalucía | es |
dc.format | application/pdf | es |
dc.format.extent | 17 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Journal of Clinical Medicine, 10 (4) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | COVID-19 | es |
dc.subject | Machine learning | es |
dc.subject | Prediction model | es |
dc.subject | SARS-CoV-2 | es |
dc.subject | Smell | es |
dc.subject | Taste | es |
dc.subject | Visual analog scale | es |
dc.title | Loss of smell and taste can accurately predict COVID-19 infection: a machine-learning approach | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Cirugía | es |
dc.relation.projectID | PAIDI2020 | es |
dc.relation.publisherversion | https://www.mdpi.com/2077-0383/10/4/570 | es |
dc.identifier.doi | 10.3390/jcm10040570 | es |
dc.journaltitle | Journal of Clinical Medicine | es |
dc.publication.volumen | 10 | es |
dc.publication.issue | 4 | es |