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

ABSTRACT: (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 u

SUBMITTER: Soriano-Arandes A 

PROVIDER: S-EPMC12031125 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature