ReviewAdvances in therapy2024
Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review.
Review in Advances in therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 41 citations in OpenAlex.
- Review
- A new era beckons in asthma: enhanced models of care, disease remission, artificial intelligence and consumer participation.Internal medicine journal · 2026Review
- The evolution of scientific knowledge in childhood asthma over time.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Triggering mechanisms of acute thunderstorm asthma: epithelial barrier disruption and immune dysregulation.Respiratory research · 2026Review
- Multimodal EHR-Based Prediction of Pediatric Asthma Exacerbations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Type 2 Inflammatory Diseases: The Crossroads of Immunity and Metabolism.Research (Washington, D.C.) · 2026Review
- Exploring Potential Stress Granule-Related Biomarkers in Childhood Asthma Using Integrated Bioinformatics and Machine Learning.Journal of inflammation research · 2026Article
- Clinical outcome prediction in pediatric respiratory infections using hybrid feature selection and a genetic algorithm-optimized machine learning.Scientific reports · 2025Article
- Asthma and severe acute respiratory infections: a stratified analysis of mortality patterns in Brazil.BMC public health · 2025Article
- Xenobiotic Toxicants and Particulate Matter: Effects, Mechanisms, Impacts on Human Health, and Mitigation Strategies.Journal of xenobiotics · 2025Review
- Utility of an artificial intelligence-based lung CT airway model in the quantitative evaluation of large and small airway lesions in patients with chronic obstructive pulmonary disease.BMC pulmonary medicine · 2025Article
- Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data.BMJ health & care informatics · 2025Article
- Predicting Asthma Exacerbations Using Machine Learning Models.Advances in therapy · 2025Article
- Artificial Intelligence in the Management of Asthma: A Review of a New Frontier in Patient Care.Journal of asthma and allergy · 2025Review
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- Complex interplays: Asthma management and maternal‑fetal outcomes in pregnancy (Review).Experimental and therapeutic medicine · 2024Review
- Perceptions Toward Using Artificial Intelligence and Technology for Asthma Attack Risk Prediction: Qualitative Exploration of Māori Views.JMIR formative research · 2024Article
- Acoustic and Clinical Data Analysis of Vocal Recordings: Pandemic Insights and Lessons.Diagnostics (Basel, Switzerland) · 2024Article
- CAR-NKT Cells in Asthma: Use of NKT as a Promising Cell for CAR Therapy.Clinical reviews in allergy & immunology · 2024Review
- Early Recognition of Secondary Asthma Caused by Lower Respiratory Tract Infection in Children Based on Multi-Omics Signature: A Retrospective Cohort Study.International journal of general medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The implementation of artificial intelligence (AI) and machine learning (ML) techniques in healthcare has garnered significant attention in recent years, especially as a result of their potential to revolutionize personalized medicine. Despite advances in the treatment and management of asthma, a significant proportion of patients continue to suffer acute exacerbations, irrespective of disease severity and therapeutic regimen. The situation is further complicated by the constellation of factors that influence disease activity in a patient with asthma, such as medical history, biomarker phenotype, pulmonary function, level of healthcare access, treatment compliance, comorbidities, personal habits, and environmental conditions. A growing body of work has demonstrated the potential for AI and ML to accurately predict asthma exacerbations while also capturing the entirety of the patient experience. However, application in the clinical setting remains mostly unexplored, and important questions on the strengths and limitations of this technology remain. This review presents an overview of the rapidly evolving landscape of AI and ML integration into asthma management by providing a snapshot of the existing scientific evidence and proposing potential avenues for future applications.
Indexed as
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What Socratic holds
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 Socratic graph.