Implementing Symptom-Based Predictive Models for Early Diagnosis of Pediatric Respiratory Viral Infections

Implementing Symptom-Based Predictive Models for Early Diagnosis of Pediatric Respiratory Viral Infections

(1) Background: Respiratory viral infections, including those caused by SARS-CoV-2, respiratory syncytial virus (RSV), influenza viruses, rhinovirus, and adenovirus, are major causes of acute respiratory infections (ARIs) in children. Symptom-based predictive models are valuable tools for expediting...

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Main Authors: Antoni Soriano-Arandes, Cristina Andrés, Aida Perramon-Malavez, Anna Creus-Costa, Anna Gatell, Ramona Martín-Martín, Elisabet Solà-Segura, Maria Teresa Riera-Bosch, Eduard Fernández, Mireia Biosca, Ramon Capdevila, Almudena Sánchez, Isabel Soler, Maria Chiné, Lidia Sanz, Gabriela Quezada, Sandra Pérez, Dolors Canadell, Olga Salvadó, Marisa Ridao, Imma Sau, Ma Àngels Rifà, Esperança Macià, Sílvia Burgaya-Subirana, Mònica Vila, Jorgina Vila, Asunción Mejías, Andrés Antón, Pere Soler-Palacin, Clara Prats
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Viruses
Subjects:
pediatric care
symptom-based predictive modeling
respiratory virus infections
acute respiratory infection
triage
Online Access:https://www.mdpi.com/1999-4915/17/4/546
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Summary:(1) Background: Respiratory viral infections, including those caused by SARS-CoV-2, respiratory syncytial virus (RSV), influenza viruses, rhinovirus, and adenovirus, are major causes of acute respiratory infections (ARIs) in children. Symptom-based predictive models are valuable tools for expediting diagnoses, particularly in primary care settings. This study assessed the effectiveness of machine learning-based models in estimating infection probabilities for these common pediatric respiratory viruses, using symptom data. (2) Methods: Data were collected from 868 children with ARI symptoms evaluated across 14 primary care centers, members of COPEDICAT (Coronavirus Pediatria Catalunya), from October 2021 to October 2023. Random forest and boosting models with 10-fold cross-validation were used, applying SMOTE-NC to address class imbalance. Model performance was evaluated via area under the curve (AUC), sensitivity, specificity, and Shapley additive explanations (SHAP) values for feature importance. (3) Results: The model performed better for RSV (AUC: 0.81, sensitivity: 0.64, specificity: 0.77) and influenza viruses (AUC: 0.71, sensitivity: 0.70, specificity: 0.59) and effectively ruled out SARS-CoV-2 based on symptom absence, such as crackles and wheezing. Predictive performance was lower for non-enveloped viruses like rhinovirus and adenovirus, due to their nonspecific symptom profiles. SHAP analysis identified key symptoms patterns for each virus. (4) Conclusions: The study demonstrated that symptom-based predictive models effectively identify pediatric respiratory infections, with notable accuracy for those caused by RSV, SARS-CoV-2, and influenza viruses.
ISSN:1999-4915

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