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- Title
Symptom-Based Predictive Model of COVID-19 Disease in Children.
- Authors
Antoñanzas, Jesús M.; Perramon, Aida; López, Cayetana; Boneta, Mireia; Aguilera, Cristina; Capdevila, Ramon; Gatell, Anna; Serrano, Pepe; Poblet, Miriam; Canadell, Dolors; Vilà, Mònica; Catasús, Georgina; Valldepérez, Cinta; Català, Martí; Soler-Palacín, Pere; Prats, Clara; Soriano-Arandes, Antoni
- 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® 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.
- Subjects
COVID-19; CORONAVIRUS diseases; PREDICTION models; MACHINE learning; COVID-19 testing
- Publication
Viruses (1999-4915), 2022, Vol 14, Issue 1, p63
- ISSN
1999-4915
- Publication type
Article
- DOI
10.3390/v14010063