Related Experiment Video
Updated: May 10, 2025
An In vitro Model to Study Immune Responses of Human Peripheral Blood Mononuclear Cells to Human Respiratory Syncytial Virus Infection
Published on: December 10, 2013
Implementing Symptom-Based Predictive Models for Early Diagnosis of Pediatric Respiratory Viral Infections
Antoni Soriano-Arandes1,2, Cristina Andrés3, Aida Perramon-Malavez4
1Pediatric Infectious Diseases and Immunodeficiencies Unit, Children's Hospital, Vall d'Hebron Barcelona Hospital Campus, 08035 Barcelona, Spain.
Insights
Machine learning models accurately predict common pediatric respiratory viruses like RSV and influenza using symptom data. These tools aid in faster diagnosis of acute respiratory infections in children.
Area of Science:
- Pediatric Infectious Diseases
- Computational Epidemiology
- Machine Learning in Healthcare
Background:
- Common pediatric respiratory viral infections include SARS-CoV-2, RSV, influenza, rhinovirus, and adenovirus.
- Acute respiratory infections (ARIs) are a significant health concern in children.
- Symptom-based predictive models can expedite ARI diagnosis in primary care.
Purpose of the Study:
- To evaluate the efficacy of machine learning models in estimating infection probabilities for common pediatric respiratory viruses using symptom data.
- To assess the diagnostic accuracy of these models in a primary care setting.
Main Methods:
- Utilized data from 868 children with ARI symptoms across 14 primary care centers (COPEDICAT cohort).
- Employed random forest and boosting models with 10-fold cross-validation and SMOTE-NC for class imbalance.
- Evaluated model performance using AUC, sensitivity, specificity, and SHAP values for feature importance.
Main Results:
- Models demonstrated higher accuracy for Respiratory Syncytial Virus (RSV) (AUC: 0.81) and influenza viruses (AUC: 0.71).
- Effectively ruled out SARS-CoV-2 infections based on the absence of specific symptoms like crackles and wheezing.
- Lower predictive performance for rhinovirus and adenovirus due to nonspecific symptoms; SHAP analysis identified key symptom patterns for each virus.
Conclusions:
- Symptom-based predictive models are effective tools for identifying pediatric respiratory infections.
- Models show notable accuracy for RSV, SARS-CoV-2, and influenza virus infections.
- Machine learning aids in differential diagnosis of viral ARIs in children based on clinical presentation.
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 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.
