ArticleScientific reports2020
Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review.Journal of clinical epidemiology · 2023Pooled it
- Machine learning-based prediction of bronchiolitis in children under two years: a multicenter study within a single metropolitan area.BMC infectious diseases · 2026Article
- Application of machine learning to predict bronchiolitis severity in children: a single-centre retrospective cohort study.European journal of pediatrics · 2026Article
- Prediction of infection in the emergency department-a machine learning model.Frontiers in artificial intelligence · 2026Article
- Development and internal-external validation of a machine learning-based risk prediction model for multidrug resistance in mechanically ventilated ICU patients with chronic obstructive pulmonary disease.Frontiers in medicine · 2026Article
- Wheezing on admission: a marker for bronchiolitis severity and asthma development.Pediatric research · 2025Article
- Merging Two Models of One-Dimensional Convolutional Neural Networks to Improve the Differential Diagnosis between Acute Asthma and Bronchitis in Preschool Children.Diagnostics (Basel, Switzerland) · 2024Article
- Integrating structured and unstructured data for timely prediction of bloodstream infection among children.Pediatric research · 2023Article
- Precision diagnostics in children.Cambridge prisms. Precision medicine · 2023Review
- Utilizing big data from electronic health records in pediatric clinical care.Pediatric research · 2023Review
- Construction of a nomogram for predicting COVID-19 in-hospital mortality: A machine learning analysis.Informatics in medicine unlocked · 2023Article
- Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach.JMIR medical informatics · 2022Article
- Machine learning early prediction of respiratory syncytial virus in pediatric hospitalized patients.Frontiers in pediatrics · 2022Article
- Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review.Journal of thoracic disease · 2021Article
- Predicting presumed serious infection among hospitalized children on central venous lines with machine learning.Computers in biology and medicine · 2021Article
- Article
- Recurrent Wheeze Exacerbations Following Acute Bronchiolitis-A Machine Learning Approach.Frontiers in allergy · 2021Article
- Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines.Frontiers in pediatrics · 2021Article
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Authors and funding
8 authors.
Funding
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 positive pressure ventilation use). Of 1,016 infants, 5.4% underwent positive pressure ventilation and 16.0% had intensive treatment. For the positive pressure ventilation outcome, machine learning models outperformed reference model (e.g., AUC 0.88 [95% CI 0.84-0.93] in gradient boosted decision tree vs 0.62 [95% CI 0.53-0.70] in reference model), with higher sensitivity (0.89 [95% CI 0.80-0.96] vs. 0.62 [95% CI 0.49-0.75]) and specificity (0.77 [95% CI 0.75-0.80] vs. 0.57 [95% CI 0.54-0.60]). The machine learning models also achieved a greater net benefit over ranges of clinical thresholds. Machine learning models consistently demonstrated a superior ability to predict acute severity and achieved greater net benefit.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.