Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study.

ABSTRACT: We aimed to develop machine learning models to accurately predict bronchiolitis severity, and to compare their predictive performance with a conventional scoring (reference) model. In a 17-center prospective study of infants (aged < 1 year) hospitalized for bronchiolitis, by using routinely-available pre-hospitalization data as predictors, we developed four machine learning models: Lasso regression, elastic net regression, random forest, and gradient boosted decision tree. We compared their predictive performance-e.g., area-under-the-curve (AUC), sensitivity, specificity, and net benefit (decision curves)-using a cross-validation method, with that of the reference model. The outcomes were positive pressure ventilation use and intensive treatment (admission to intensive care unit and/or pos

SUBMITTER: Raita Y 

PROVIDER: S-EPMC7335203 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature