tetano
Editor, Senior Moderator
Viruses
. 2021 Dec 30;14(1):63.
doi: 10.3390/v14010063.
Symptom-Based Predictive Model of COVID-19 Disease in Children
Jesús M Antoñanzas[SUP] 1 [/SUP], Aida Perramon[SUP] 2 [/SUP], Cayetana López[SUP] 1 [/SUP], Mireia Boneta[SUP] 1 [/SUP], Cristina Aguilera[SUP] 1 [/SUP], Ramon Capdevila[SUP] 3 [/SUP], Anna Gatell[SUP] 4 [/SUP], Pepe Serrano[SUP] 4 [/SUP], Miriam Poblet[SUP] 5 [/SUP], Dolors Canadell[SUP] 6 [/SUP], Mònica Vilà[SUP] 7 [/SUP], Georgina Catasús[SUP] 8 [/SUP], Cinta Valldepérez[SUP] 4 [/SUP], Martí Català[SUP] 2 9 [/SUP], Pere Soler-Palacín[SUP] 10 [/SUP], Clara Prats[SUP] 2 [/SUP], Antoni Soriano-Arandes[SUP] 10 [/SUP], The Copedi-Cat Research Group
Affiliations
Abstract
Background: Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is neither always accessible nor easy to perform in children. We aimed to propose a machine learning model to assess the need for a SARS-CoV-2 test in children (<16 years old), depending on their clinical symptoms.
Methods: Epidemiological and clinical data were obtained from the REDCap[SUP]®[/SUP] registry. Overall, 4434 SARS-CoV-2 tests were performed in symptomatic children between 1 November 2020 and 31 March 2021, 784 were positive (17.68%). We pre-processed the data to be suitable for a machine learning (ML) algorithm, balancing the positive-negative rate and preparing subsets of data by age. We trained several models and chose those with the best performance for each subset.
Results: The use of ML demonstrated an AUROC of 0.65 to predict a COVID-19 diagnosis in children. The absence of high-grade fever was the major predictor of COVID-19 in younger children, whereas loss of taste or smell was the most determinant symptom in older children.
Conclusions: Although the accuracy of the models was lower than expected, they can be used to provide a diagnosis when epidemiological data on the risk of exposure to COVID-19 is unknown.
Keywords: COVID-19; SARS-CoV-2; deep learning; epidemiology; machine learning; microbiology; paediatrics.
. 2021 Dec 30;14(1):63.
doi: 10.3390/v14010063.
Symptom-Based Predictive Model of COVID-19 Disease in Children
Jesús M Antoñanzas[SUP] 1 [/SUP], Aida Perramon[SUP] 2 [/SUP], Cayetana López[SUP] 1 [/SUP], Mireia Boneta[SUP] 1 [/SUP], Cristina Aguilera[SUP] 1 [/SUP], Ramon Capdevila[SUP] 3 [/SUP], Anna Gatell[SUP] 4 [/SUP], Pepe Serrano[SUP] 4 [/SUP], Miriam Poblet[SUP] 5 [/SUP], Dolors Canadell[SUP] 6 [/SUP], Mònica Vilà[SUP] 7 [/SUP], Georgina Catasús[SUP] 8 [/SUP], Cinta Valldepérez[SUP] 4 [/SUP], Martí Català[SUP] 2 9 [/SUP], Pere Soler-Palacín[SUP] 10 [/SUP], Clara Prats[SUP] 2 [/SUP], Antoni Soriano-Arandes[SUP] 10 [/SUP], The Copedi-Cat Research Group
Affiliations
- PMID: 35062267
- DOI: 10.3390/v14010063
Abstract
Background: Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is neither always accessible nor easy to perform in children. We aimed to propose a machine learning model to assess the need for a SARS-CoV-2 test in children (<16 years old), depending on their clinical symptoms.
Methods: Epidemiological and clinical data were obtained from the REDCap[SUP]®[/SUP] registry. Overall, 4434 SARS-CoV-2 tests were performed in symptomatic children between 1 November 2020 and 31 March 2021, 784 were positive (17.68%). We pre-processed the data to be suitable for a machine learning (ML) algorithm, balancing the positive-negative rate and preparing subsets of data by age. We trained several models and chose those with the best performance for each subset.
Results: The use of ML demonstrated an AUROC of 0.65 to predict a COVID-19 diagnosis in children. The absence of high-grade fever was the major predictor of COVID-19 in younger children, whereas loss of taste or smell was the most determinant symptom in older children.
Conclusions: Although the accuracy of the models was lower than expected, they can be used to provide a diagnosis when epidemiological data on the risk of exposure to COVID-19 is unknown.
Keywords: COVID-19; SARS-CoV-2; deep learning; epidemiology; machine learning; microbiology; paediatrics.