SynthesisEuropean respiratory review : an official journal of the European Respiratory Society2024
Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis.
Synthesis in European respiratory review : an official journal of the European Respiratory Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).Journal of the National Medical Association · 2026Review
- Evolution, hotspots, and future directions of artificial intelligence in asthma research: a Web of Science-based bibliometric analysis [2016-2026].Journal of thoracic disease · 2026Article
- Harnessing Machine Learning and Electronic Health Record Data to Improve Asthma Management.Current allergy and asthma reports · 2026Review
- AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS).NPJ digital medicine · 2026Article
- The evolution of scientific knowledge in childhood asthma over time.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Article
- The Evolving Role of Artificial Intelligence in Pediatric Asthma Management: Opportunities and Challenges for Modern Healthcare.Journal of personalized medicine · 2026Review
- Reimagining paediatric care: technology, trust, and the global movement for child-centred innovation.Frontiers in medicine · 2026Review
- Multimodal EHR-Based Prediction of Pediatric Asthma Exacerbations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Management of Patients With Comorbid Asthma and Obesity: A Large Language Model Evaluation of Clinical Documentation.The journal of allergy and clinical immunology. In practice · 2026Article
- The pan-immune-inflammation value predicts stroke-associated pneumonia and poor outcome in spontaneous intracerebral hemorrhage: a machine learning approach.Frontiers in neurology · 2026Article
- Innovative diagnostic techniques and their clinical implications in food allergy: current clinical practice and future perspectives.Frontiers in allergy · 2026Review
- Preparing Allergists to Practice in 2050 Using Artificial Intelligence.The journal of allergy and clinical immunology. In practice · 2025Review
- Developing a prediction model for persistent airflow limitation in asthmatic children.Journal of thoracic disease · 2025Article
- Development of an explainable machine learning asthma prediction model using serum brominated flame retardants in a national population.Clinical and experimental medicine · 2025Article
- Asthma and severe acute respiratory infections: a stratified analysis of mortality patterns in Brazil.BMC public health · 2025Article
- Pediatrics 4.0: the Transformative Impacts of the Latest Industrial Revolution on Pediatrics.Health care analysis : HCA : journal of health philosophy and policy · 2025Article
- A support vector machine-based tool for rapid pediatric asthma exacerbation risk assessment: development and nursing application.Frontiers in pediatrics · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAsthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exist, artificial intelligence (AI) offers the potential for enhanced prediction models.
objectiveThis study aims to systematically evaluate and quantify the performance of machine learning (ML) algorithms in predicting the risk of hospitalisation and emergency department (ED) admission for acute asthma exacerbations in children.
methodsWe performed a systematic review with meta-analysis, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The risk of bias and applicability for eligible studies was assessed according to the prediction model study risk of bias assessment tool (PROBAST). The protocol of our systematic review was registered in the International Prospective Register of Systematic Reviews.
resultsOur meta-analysis included seven articles encompassing a total of 17 ML-based prediction models. We found a pooled area under the curve (AUC) of 0.67 (95% CI 0.61-0.73; I
conclusionThis study provides the most comprehensive assessment of AI-based algorithms in predicting paediatric asthma exacerbations to date. While these models show promise and ML-based hospitalisation prediction models, in particular, demonstrate good accuracy, further external validation is needed before these models can be reliably implemented in real-life clinical practice.
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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.