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

Authors :
Youcef Azeli
Alberto Fernández
Federico Capriles
Wojciech Rojewski
Vanesa Lopez-Madrid
David Sabaté-Lissner
Rosa Maria Serrano
Cristina Rey-Reñones
Marta Civit
Josefina Casellas
Abdelghani El Ouahabi-El Ouahabi
Maria Foglia-Fernández
Salvador Sarrá
Eduard Llobet
Source :
Scientific Reports, Vol 12, Iss 1, Pp 1-9 (2022)
Publication Year :
2022
Publisher :
Nature Portfolio, 2022.

Abstract

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.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
20452322
Volume :
12
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.6b81b768f0e44ea1a28b36c3fa6f7fae
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-022-19817-x