ArticleClinical and translational allergy2021
Development of childhood asthma prediction models using machine learning approaches.
Article in Clinical and translational allergy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 4 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- Artificial intelligence-based models in predicting acute exacerbations and diagnosing pediatric asthma: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Respiratory virus type to guide predictive enrichment approaches in the management of the first episode of bronchiolitis: A systematic review.Frontiers in immunology · 2022Pooled it
- Predictive models for personalized asthma attacks based on patient's biosignals and environmental factors: a systematic review.BMC medical informatics and decision making · 2021Pooled it
- Precision medicine to diagnose asthma in preschool children: comparison of clinical scores, lung function, biomarkers, and genetic tests.Paediatric respiratory reviews · 2026Review
- Prediction of allergic disease trajectories from birth up to adolescence.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2026Article
- Explainable artificial intelligence-driven ensemble learning for asthma risk prediction using machine and deep learning.The Journal of international medical research · 2026Article
- Deconvolution of molecular mechanisms in di-n-butyl phthalate/mono-n-butyl phthalate induced diabetic kidney disease by integrated machine learning and molecular docking.BMC nephrology · 2025Article
- Machine learning-based early prediction of asthma in preschoolers: The COCOA birth cohort study.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025Article
- Enhancing the Prediction of Inborn Errors of Immunity: Integrating Jeffrey Modell Foundation Criteria with Clinical Variables Using Machine Learning.Children (Basel, Switzerland) · 2025Article
- AI model for predicting asthma prognosis in children.The journal of allergy and clinical immunology. Global · 2025Article
- Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset.Scientific reports · 2025Article
- Article
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- Association of urinary eosinophilic protein X at age 3 years and subsequent persistence of wheezing and asthma diagnosis in adolescence.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2024Article
- Bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study.BMC medical research methodology · 2024Article
- The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment.Current allergy and asthma reports · 2024Review
- Asthma prediction via affinity graph enhanced classifier: a machine learning approach based on routine blood biomarkers.Journal of translational medicine · 2024Article
- Artificial intelligence and wheezing in children: where are we now?Frontiers in medicine · 2024Review
- Prediction of Postnatal Growth Failure in Very Low Birth Weight Infants Using a Machine Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundRespiratory symptoms are common in early life and often transient. It is difficult to identify in which children these will persist and result in asthma. Machine learning (ML) approaches have the potential for better predictive performance and generalisability over existing childhood asthma prediction models. This study applied ML approaches to predict school-age asthma (age 10) in early life (Childhood Asthma Prediction in Early life, CAPE model) and at preschool age (Childhood Asthma Prediction at Preschool age, CAPP model).
methodsClinical and environmental exposure data was collected from children enrolled in the Isle of Wight Birth Cohort (N = 1368, ∼15% asthma prevalence). Recursive Feature Elimination (RFE) identified an optimal subset of features predictive of school-age asthma for each model. Seven state-of-the-art ML classification algorithms were used to develop prognostic models. Training was performed by applying fivefold cross-validation, imputation, and resampling. Predictive performance was evaluated on the test set. Models were further externally validated in the Manchester Asthma and Allergy Study (MAAS) cohort.
resultsRFE identified eight and twelve predictors for the CAPE and CAPP models, respectively. Support Vector Machine (SVM) algorithms provided the best performance for both the CAPE (area under the receiver operating characteristic curve, AUC = 0.71) and CAPP (AUC = 0.82) models. Both models demonstrated good generalisability in MAAS (CAPE 8-year = 0.71, 11-year = 0.71, CAPP 8-year = 0.83, 11-year = 0.79) and excellent sensitivity to predict a subgroup of persistent wheezers.
conclusionUsing ML approaches improved upon the predictive performance of existing regression-based models, with good generalisability and ability to rule in asthma and predict persistent wheeze.
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Registered trials
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.