ArticleScientific reports2022
Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- Pooled it
- Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.Journal of medical systems · 2024Pooled it
- Machine learning for prediction of asthma exacerbations among asthmatic patients: a systematic review and meta-analysis.BMC pulmonary medicine · 2023Pooled it
- Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors.BMJ open respiratory research · 2025Trial
- 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
- Machine learning models to predict 6-month mortality risk in home-based hospice patients with advanced cancer.Asia-Pacific journal of oncology nursing · 2025Article
- Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.International journal of medical informatics · 2025Article
- Digitally mapping the asthma journey-from diagnosis to remission.EClinicalMedicine · 2025Review
- Article
- Fast binary logistic regression.PeerJ. Computer science · 2025Article
- 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
- Conceptualizing bias in EHR data: A case study in performance disparities by demographic subgroups for a pediatric obesity incidence classifier.PLOS digital health · 2024Article
- Comparison of machine-learning and logistic regression models for prediction of 30-day unplanned readmission in electronic health records: A development and validation study.PLOS digital health · 2024Article
- Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review.Advances in therapy · 2024Review
- Analysis of factors affecting nonalcoholic fatty liver disease in Chinese steel workers and risk assessment studies.Lipids in health and disease · 2023Article
- Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England.Pragmatic and observational research · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early detection of severe asthma exacerbations through home monitoring data in patients with stable mild-to-moderate chronic asthma could help to timely adjust medication. We evaluated the potential of machine learning methods compared to a clinical rule and logistic regression to predict severe exacerbations. We used daily home monitoring data from two studies in asthma patients (development: n = 165 and validation: n = 101 patients). Two ML models (XGBoost, one class SVM) and a logistic regression model provided predictions based on peak expiratory flow and asthma symptoms. These models were compared with an asthma action plan rule. Severe exacerbations occurred in 0.2% of all daily measurements in the development (154/92,787 days) and validation cohorts (94/40,185 days). The AUC of the best performing XGBoost was 0.85 (0.82-0.87) and 0.88 (0.86-0.90) for logistic regression in the validation cohort. The XGBoost model provided overly extreme risk estimates, whereas the logistic regression underestimated predicted risks. Sensitivity and specificity were better overall for XGBoost and logistic regression compared to one class SVM and the clinical rule. We conclude that ML models did not beat logistic regression in predicting short-term severe asthma exacerbations based on home monitoring data. Clinical application remains challenging in settings with low event incidence and high false alarm rates with high sensitivity.
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