2026-04-17T06:19:32Zhttps://recercat.cat/oai/request

oai:recercat.cat:11351/77382026-03-01T00:46:33Zcom_2072_378070com_2072_378040col_2072_378092

Symptom-Based Predictive Model of COVID-19 Disease in Children López, Cayetana Boneta, Mireia Capdevila, Ramon Soriano-Arandes, Antoni Perramon, Aida Aguilera, Cristina Antoñanzas, Jesús M. Soler-Palacin, Pere Institut Català de la Salut [Antoñanzas JM, López C, Boneta M, Aguilera C] Barcelona School of Informatics, Universitat Politècnica de Catalunya (UPC⋅BarcelonaTech), Barcelona, Spain. [Perramon A] Department of Physics, Universitat Politècnica de Catalunya (UPC⋅BarcelonaTech), Barcelona, Spain. [Capdevila R] ABS Borges Blanques, Institut Català de Salut (ICS), Lleida, Spain. [Soler-Palacín P, Soriano-Arandes A] Unitat de Patologia Infecciosa i Immunodeficiències de Pediatria, Vall d’Hebron Hospital Universitari, Barcelona, Spain Vall d'Hebron Barcelona Hospital Campus COVID-19 (Malaltia) - Diagnòstic Aprenentatge automàtic Diagnòstic de laboratori ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Diagnostic Techniques and Procedures::Clinical Laboratory Techniques::Clinical Chemistry Tests PHENOMENA AND PROCESSES::Mathematical Concepts::Algorithms::Artificial Intelligence::Machine Learning DISEASES::Virus Diseases::RNA Virus Infections::Nidovirales Infections::Coronaviridae Infections::Coronavirus Infections Other subheadings::Other subheadings::/diagnosis TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::técnicas y procedimientos diagnósticos::técnicas de laboratorio clínico::pruebas de bioquímica clínica FENÓMENOS Y PROCESOS::conceptos matemáticos::algoritmos::inteligencia artificial::aprendizaje automático ENFERMEDADES::virosis::infecciones por virus ARN::infecciones por Nidovirales::infecciones por Coronaviridae::infecciones por Coronavirus Otros calificadores::Otros calificadores::/diagnóstico COVID-19; Microbiology; Paediatrics COVID-19; Microbiología; Pediatría COVID-19; Microbiologia; Pediatria 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. This research has received external funding from the Fundació la Marató tv3 after being awarded in the COVID-19 research call with the expedient number 202134-30-31. 2022-06-28T10:34:32Z 2022-06-28T10:34:32Z 2022-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Antoñanzas JM, Perramon A, López C, Boneta M, Aguilera C, Capdevila R, et al. Symptom-Based Predictive Model of COVID-19 Disease in Children. Viruses. 2022 Dec 30;14(1):63. 1999-4915 https://hdl.handle.net/11351/7738 10.3390/v14010063 35062267 eng Viruses;14(1) https://doi.org/10.3390/v14010063 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess application/pdf application/pdf MDPI Scientia