ArticleAnnals of the American Thoracic Society2018
Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.
Article in Annals of the American Thoracic Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- Comparative response to pharmacological management of asthma in overweight/obese childrenEuropean respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Early initiation of inhaled corticosteroid-long-acting βPediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2026Article
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 2026Review
- Unsupervised Machine Learning Identifies Asthma Phenotypes in the Population-Based West Sweden Asthma Study.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2026Article
- Prevalence and predictors of asthma among Indian women: a machine learning-based analysis of NFHS-5 data.BMC public health · 2025Article
- AI model for predicting asthma prognosis in children.The journal of allergy and clinical immunology. Global · 2025Article
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Asthma prediction via affinity graph enhanced classifier: a machine learning approach based on routine blood biomarkers.Journal of translational medicine · 2024Article
- Asthma clustering methods: a literature-informed application to the children's health study data.The Journal of asthma : official journal of the Association for the Care of Asthma · 2022Review
- A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research-A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee.The journal of allergy and clinical immunology. In practice · 2022Article
- Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.BMJ open respiratory research · 2022Article
- Obesity Is Associated with Sustained Symptomatology and Unique Inflammatory Features in Children with Asthma.The journal of allergy and clinical immunology. In practice · 2022Article
- Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma.Children (Basel, Switzerland) · 2021Article
- Resolving Clinical Phenotypes into Endotypes in Allergy: Molecular and Omics Approaches.Clinical reviews in allergy & immunology · 2021Review
- Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease.International journal of medical sciences · 2021Review
- Treatment selection using prototyping in latent-space with application to depression treatment.PloS one · 2021Article
- Hypersensitivity Reactions: An Everyday Occurrence in Pediatric Allergy Clinics.Pediatric allergy, immunology, and pulmonology · 2020Review
- From Clinic to Computer and Back Again: Practical Considerations When Designing and Implementing Machine Learning Solutions for Pediatrics.Current treatment options in pediatrics · 2020Review
- Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work.European journal of medicinal chemistry · 2019Review
- Artificial intelligence for precision education in radiology.The British journal of radiology · 2019Review
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
Abstract
rationalePediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based on the relationship between treatment and clinical outcome has not been performed, and machine learning approaches for long-term outcome prediction in pediatric asthma have not been studied in depth.
objectivesOur objectives were to use our novel machine learning algorithm, predictor pursuit (PP), to discover pediatric asthma phenotypes on the basis of asthma control in response to controller medications, to predict longitudinal asthma control among children with asthma, and to identify features associated with asthma control within each discovered pediatric phenotype.
methodsWe applied PP to the Childhood Asthma Management Program study data (n = 1,019) to discover phenotypes on the basis of asthma control between assigned controller therapy groups (budesonide vs. nedocromil). We confirmed PP's ability to discover phenotypes using the Asthma Clinical Research Network/Childhood Asthma Research and Education network data. We next predicted children's asthma control over time and compared PP's performance with that of traditional prediction methods. Last, we identified clinical features most correlated with asthma control in the discovered phenotypes.
resultsFour phenotypes were discovered in both datasets: allergic not obese (A
conclusionsAdvanced statistical machine learning approaches can be powerful tools for discovery of phenotypes based on treatment response and can aid in asthma control prediction in complex medical conditions such as asthma.
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Registered trials
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