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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...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-7913595 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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