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
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
Entity: Universitat Rovira i Virgili
Journal publication year: 2022
Publication Type: Journal Publications
Repositori URV