Selected article for: "loss smell and machine learning"
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
Title: Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach
  • Cord-id: 3f98q44j
  • Document date: 2021_2_3
    • ID: 3f98q44j
    Snippet: 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 re
    Document: 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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