ArticlePloS one2021
Personalized prediction of early childhood asthma persistence: A machine learning approach.
Article in PloS one, 2021. 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, 58 citations in OpenAlex.
- Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.Journal of medical systems · 2024Pooled it
- Telemedicine in pediatrics: a critical narrative review of innovations, limitations, and future priorities.European journal of pediatrics · 2026Review
- Respiratory sound-based AI screening of asthma and COPD via multi-feature fusion and CatBoost classification.Scientific reports · 2026Article
- AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS).NPJ digital medicine · 2026Article
- Explainable artificial intelligence-driven ensemble learning for asthma risk prediction using machine and deep learning.The Journal of international medical research · 2026Article
- The Evolving Role of Artificial Intelligence in Pediatric Asthma Management: Opportunities and Challenges for Modern Healthcare.Journal of personalized medicine · 2026Review
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- A personalized prediction of longitudinal growth using People-Like-Me methods.Computers in biology and medicine · 2025Article
- Leveraging artificial intelligence for the management of preschool wheeze: A narrative review.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025Review
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- Bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study.BMC medical research methodology · 2024Article
- Perceptions Toward Using Artificial Intelligence and Technology for Asthma Attack Risk Prediction: Qualitative Exploration of Māori Views.JMIR formative research · 2024Article
- Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review.JMIR AI · 2024Article
- The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment.Current allergy and asthma reports · 2024Review
- Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in Pediatrics.Journal of medical Internet research · 2024Article
- Article
- The Use of Electronic Health Record Data to Identify Variation in Referral, Consent, and Engagement in a Pediatric Intervention for Overweight and Obesity: A Cross-Sectional Study.Population health management · 2023Article
- Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.BMJ open respiratory research · 2022Article
- Using machine learning for the personalised prediction of revision endoscopic sinus surgery.PloS one · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Early childhood asthma diagnosis is common; however, many children diagnosed before age 5 experience symptom resolution and it remains difficult to identify individuals whose symptoms will persist. Our objective was to develop machine learning models to identify which individuals diagnosed with asthma before age 5 continue to experience asthma-related visits. We curated a retrospective dataset for 9,934 children derived from electronic health record (EHR) data. We trained five machine learning models to differentiate individuals without subsequent asthma-related visits (transient diagnosis) from those with asthma-related visits between ages 5 and 10 (persistent diagnosis) given clinical information up to age 5 years. Based on average NPV-Specificity area (ANSA), all models performed significantly better than random chance, with XGBoost obtaining the best performance (0.43 mean ANSA). Feature importance analysis indicated age of last asthma diagnosis under 5 years, total number of asthma related visits, self-identified black race, allergic rhinitis, and eczema as important features. Although our models appear to perform well, a lack of prior models utilizing a large number of features to predict individual persistence makes direct comparison infeasible. However, feature importance analysis indicates our models are consistent with prior research indicating diagnosis age and prior health service utilization as important predictors of persistent asthma. We therefore find that machine learning models can predict which individuals will experience persistent asthma with good performance and may be useful to guide clinician and parental decisions regarding asthma counselling in early childhood.
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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.