ArticleChest2021
Novel Machine Learning Can Predict Acute Asthma Exacerbation.
Article in Chest, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 4 of them syntheses that pooled it.
What it found
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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
44 citing papers in PubMed, 4 syntheses or guidelines pooled it, 92 citations in OpenAlex.
- Application of type 2 inflammation-related biomarkers in risk prediction models for acute exacerbations in adult asthma: a systematic review.Frontiers in medicine · 2026Pooled it
- Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.Journal of medical systems · 2024Pooled it
- Association of humidity and precipitation with asthma: a systematic review and meta-analysis.Frontiers in allergy · 2024Pooled it
- Machine learning for prediction of asthma exacerbations among asthmatic patients: a systematic review and meta-analysis.BMC pulmonary medicine · 2023Pooled it
- 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
- Cohort vs case-control design for transformer-based prediction of asthma exacerbations in mild asthma.NPJ digital medicine · 2026Article
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 2026Review
- Development and validation of a long-term survival prediction model for older adults with asthma.Archives of public health = Archives belges de sante publique · 2026Article
- The Evolving Role of Artificial Intelligence in Pediatric Asthma Management: Opportunities and Challenges for Modern Healthcare.Journal of personalized medicine · 2026Review
- Construction and Validation of Active Case-Finding Tool in Community Participants with Chronic Obstructive Pulmonary Disease Using an Interpretable Machine Learning Approach.International journal of chronic obstructive pulmonary disease · 2026Article
- Multimodal EHR-Based Prediction of Pediatric Asthma Exacerbations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Machine learning models incorporating genotype and ancestry improve severe asthma risk prediction.Scientific reports · 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
- AI model for predicting asthma prognosis in children.The journal of allergy and clinical immunology. Global · 2025Article
- Mitochondrial DNA copy number variation in asthma risk, severity, and exacerbations.The Journal of allergy and clinical immunology · 2025Article
- Asthma Exacerbation Risk in Pregnancy and Postpartum: Assessing the Impact of Gestational Diabetes Mellitus and Other Key Factors.The journal of allergy and clinical immunology. In practice · 2025Article
- Forecasting Hospitalization for Adult Asthma Patients in Emergency Departments Based on Multiple Environmental and Clinical Factors.Journal of asthma and allergy · 2025Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAsthma exacerbations result in significant health and economic burden, but are difficult to predict. RESEARCH QUESTION: Can machine learning (ML) models with large-scale outpatient data predict asthma exacerbations? STUDY DESIGN AND
methodsWe analyzed data extracted from electronic health records (EHRs) of asthma patients treated at the Cleveland Clinic from 2010 through 2018. Demographic information, comorbidities, laboratory values, and asthma medications were included as covariates. Three different models were built with logistic regression, random forests, and a gradient boosting decision tree to predict: (1) nonsevere asthma exacerbation requiring oral glucocorticoid burst, (2) ED visits, and (3) hospitalizations.
resultsOf 60,302 patients, 19,772 (32.8%) had at least one nonsevere exacerbation requiring oral glucocorticoid burst, 1,748 (2.9%) requiring and ED visit and 902 (1.5%) requiring hospitalization. Nonsevere exacerbation, ED visit, and hospitalization were predicted best by light gradient boosting machine, an algorithm used in ML to fit predictive analytic models, and had an area under the receiver operating characteristic curve of 0.71 (95% CI, 0.70-0.72), 0.88 (95% CI, 0.86-0.89), and 0.85 (95% CI, 0.82-0.88), respectively. Risk factors for all three outcomes included age, long-acting β agonist, high-dose inhaled glucocorticoid, or chronic oral glucocorticoid therapy. In subgroup analysis of 9,448 patients with spirometry data, low FEV
interpretationModels built with an ML algorithm from real-world outpatient EHR data accurately predicted asthma exacerbation and can be incorporated into clinical decision tools to enhance outpatient care and to prevent adverse outcomes.
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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.