ArticleFrontiers in pediatrics2026
Leveraging machine learning models to forecast pediatric allergic rhinitis exacerbation risk based on environmental exposure data.
Article in Frontiers in pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Acute exacerbations of pediatric allergic rhinitis (AR) are difficult to anticipate, and individualized risk tools integrating clinical and environmental data are lacking. We developed an interpretable machine-learning model to predict 90-day AR exacerbation risk in children and conducted a preliminary transportability assessment in an independent cohort, following TRIPOD + AI guidelines. Methods: A development cohort ( Results: Exacerbation occurred in 34.4% (687/2,000) of the development cohort and 36.0% (18/50) of the external cohort. The primary LR model achieved an internal AUC of 0.81 (95% CI: 0.74-0.88) and an external AUC of 0.71 (95% CI: 0.50-0.92); the wide confidence interval reflects the limited precision of the small validation sample. Baseline TNSS, same-day PM Conclusion: An interpretable LR model integrating baseline symptom burden with ambient PM
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