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Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach

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 w...

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Autores principales: Callejon-Leblic, María A, Moreno-Luna, Ramon, Del Cuvillo, Alfonso, Reyes-Tejero, Isabel M, Garcia-Villaran, Miguel A, Santos-Peña, Marta, Maza-Solano, Juan M, Martín-Jimenez, Daniel I, Palacios-Garcia, Jose M, Fernandez-Velez, Carlos, Gonzalez-Garcia, Jaime, Sanchez-Calvo, Juan M, Solanellas-Soler, Juan, Sanchez-Gomez, Serafin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913595/
https://www.ncbi.nlm.nih.gov/pubmed/33546319
http://dx.doi.org/10.3390/jcm10040570
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author Callejon-Leblic, María A
Moreno-Luna, Ramon
Del Cuvillo, Alfonso
Reyes-Tejero, Isabel M
Garcia-Villaran, Miguel A
Santos-Peña, Marta
Maza-Solano, Juan M
Martín-Jimenez, Daniel I
Palacios-Garcia, Jose M
Fernandez-Velez, Carlos
Gonzalez-Garcia, Jaime
Sanchez-Calvo, Juan M
Solanellas-Soler, Juan
Sanchez-Gomez, Serafin
author_facet Callejon-Leblic, María A
Moreno-Luna, Ramon
Del Cuvillo, Alfonso
Reyes-Tejero, Isabel M
Garcia-Villaran, Miguel A
Santos-Peña, Marta
Maza-Solano, Juan M
Martín-Jimenez, Daniel I
Palacios-Garcia, Jose M
Fernandez-Velez, Carlos
Gonzalez-Garcia, Jaime
Sanchez-Calvo, Juan M
Solanellas-Soler, Juan
Sanchez-Gomez, Serafin
author_sort Callejon-Leblic, María A
collection PubMed
description 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 reverse-transcription 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.
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spelling pubmed-79135952021-02-28 Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach Callejon-Leblic, María A Moreno-Luna, Ramon Del Cuvillo, Alfonso Reyes-Tejero, Isabel M Garcia-Villaran, Miguel A Santos-Peña, Marta Maza-Solano, Juan M Martín-Jimenez, Daniel I Palacios-Garcia, Jose M Fernandez-Velez, Carlos Gonzalez-Garcia, Jaime Sanchez-Calvo, Juan M Solanellas-Soler, Juan Sanchez-Gomez, Serafin J Clin Med Article 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 reverse-transcription 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. MDPI 2021-02-03 /pmc/articles/PMC7913595/ /pubmed/33546319 http://dx.doi.org/10.3390/jcm10040570 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Callejon-Leblic, María A
Moreno-Luna, Ramon
Del Cuvillo, Alfonso
Reyes-Tejero, Isabel M
Garcia-Villaran, Miguel A
Santos-Peña, Marta
Maza-Solano, Juan M
Martín-Jimenez, Daniel I
Palacios-Garcia, Jose M
Fernandez-Velez, Carlos
Gonzalez-Garcia, Jaime
Sanchez-Calvo, Juan M
Solanellas-Soler, Juan
Sanchez-Gomez, Serafin
Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach
title Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach
title_full Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach
title_fullStr Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach
title_full_unstemmed Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach
title_short Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach
title_sort loss of smell and taste can accurately predict covid-19 infection: a machine-learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913595/
https://www.ncbi.nlm.nih.gov/pubmed/33546319
http://dx.doi.org/10.3390/jcm10040570
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