ArticleAnnals of the New York Academy of Sciences2017
Machine learning approaches to personalize early prediction of asthma exacerbations.
Article in Annals of the New York Academy of Sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 7 of them syntheses that pooled it.
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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.
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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.
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Who cites it
64 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Pooled it
- Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.Journal of medical systems · 2024Pooled it
- Nurse-led Telehealth Intervention for Rehabilitation (Telerehabilitation) Among Community-Dwelling Patients With Chronic Diseases: Systematic Review and Meta-analysis.Journal of medical Internet research · 2022Pooled it
- Predictive models for personalized asthma attacks based on patient's biosignals and environmental factors: a systematic review.BMC medical informatics and decision making · 2021Pooled it
- A systematic review of methodology used in the development of prediction models for future asthma exacerbation.BMC medical research methodology · 2020Pooled 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
- Explainable artificial intelligence-driven ensemble learning for asthma risk prediction using machine and deep learning.The Journal of international medical research · 2026Article
- Applications of AI/ML in accelerating the development of pulmonary drug delivery system.Acta pharmaceutica Sinica. B · 2026Review
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- AI Applications for Chronic Condition Self-Management: Scoping Review.Journal of medical Internet research · 2025Article
- Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset.Scientific reports · 2025Article
- Prediction of daily childhood asthma exacerbation from ambient meteorological, environmental risk factors and respiratory viruses, Philadelphia, PA, 2011 to 2016.Environmental science and pollution research international · 2025Article
- Artificial Intelligence in the Management of Asthma: A Review of a New Frontier in Patient Care.Journal of asthma and allergy · 2025Review
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Assessing the Impact of New Technologies on Managing Chronic Respiratory Diseases.Journal of clinical medicine · 2024Review
- Perceptions Toward Using Artificial Intelligence and Technology for Asthma Attack Risk Prediction: Qualitative Exploration of Māori Views.JMIR formative research · 2024Article
- Artificial intelligence in respiratory care: perspectives on critical opportunities and challenges.Breathe (Sheffield, England) · 2024Review
- Enhancing Adult Asthma Management: A Review on the Utility of Remote Home Spirometry and Mobile Applications.Journal of personalized medicine · 2024Review
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been systematically evaluated. The aim of this study was to explore the utility of telemonitoring data for building machine learning algorithms that predict asthma exacerbations before they occur. The study dataset comprised daily self-monitoring reports consisting of 7001 records submitted by adult asthma patients during home telemonitoring. Predictive modeling included preparation of stratified training datasets, predictive feature selection, and evaluation of resulting classifiers. Using a 7-day window, a naive Bayesian classifier, adaptive Bayesian network, and support vector machines were able to predict asthma exacerbation occurring on day 8, with sensitivity of 0.80, 1.00, and 0.84; specificity of 0.77, 1.00, and 0.80; and accuracy of 0.77, 1.00, and 0.80, respectively. Our study demonstrated that machine learning techniques have significant potential in developing personalized decision support for chronic disease telemonitoring systems. Future studies may benefit from a comprehensive predictive framework that combines telemonitoring data with other factors affecting the likelihood of developing acute exacerbation. Approaches implemented for advanced asthma exacerbation prediction may be extended to prediction of exacerbations in patients with other chronic health conditions.
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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.