ArticleJournal of asthma and allergy2022
A Predictive Machine Learning Tool for Asthma Exacerbations: Results from a 12-Week, Open-Label Study Using an Electronic Multi-Dose Dry Powder Inhaler with Integrated Sensors.
Article in Journal of asthma and allergy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 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
25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Artificial Intelligence Applications in Medical Devices for Personalized Health Care Solutions: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.Journal of medical systems · 2024Pooled it
- Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors.BMJ open respiratory research · 2025Trial
- Albuterol Digihaler in COPD Disease Management: A Real-World Study to Assess Digihaler Inhalation Parameters, Thresholds and Their Use to Identify Deterioration in Clinical Practice.International journal of chronic obstructive pulmonary disease · 2025Trial
- Article
- Explainable artificial intelligence-driven ensemble learning for asthma risk prediction using machine and deep learning.The Journal of international medical research · 2026Article
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Artificial Intelligence-Driven Wearable and Connected Technology for Allergy: Real-Time Monitoring and Predictive Management for Personalized Care.The journal of allergy and clinical immunology. In practice · 2025Review
- Multidisciplinary collaboration is key to advancement in respiratory medicine! Interview with the ECM award winner 2024.Breathe (Sheffield, England) · 2025Article
- Digitally mapping the asthma journey-from diagnosis to remission.EClinicalMedicine · 2025Review
- Does adding digital inhalers to asthma triple therapy result in quadruple therapy?ERJ open research · 2025Article
- Medication nonadherence - definition, measurement, prevalence, and causes: reflecting on the past 20 years and looking forwards.Frontiers in pharmacology · 2025Review
- An Updated Systematic Review on Asthma Exacerbation Risk Prediction Models Between 2017 and 2023: Risk of Bias and Applicability.Journal of asthma and allergy · 2025Review
- Digital markers of asthma exacerbations: a systematic review.ERJ open research · 2024Review
- Flowrate Sensing and Measurement in Portable Smart Inhalers.Sensors (Basel, Switzerland) · 2024Review
- Uncovering patterns of inhaler technique and reliever use: the value of objective, personalized data from a digital inhaler.NPJ primary care respiratory medicine · 2024Article
- DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol.BMJ open respiratory research · 2024Article
- Effect of Individual Patient Characteristics and Treatment Choices on Reliever Medication Use in Moderate-Severe Asthma: A Poisson Analysis of Randomised Clinical Trials.Advances in therapy · 2024Article
- Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review.Advances in therapy · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Machine learning models informed by sensor data inputs have the potential to provide individualized predictions of asthma deterioration. This study aimed to determine if data from an integrated digital inhaler could be used to develop a machine learning model capable of predicting impending exacerbations. Patients and Methods: Adult patients with poorly controlled asthma were enrolled in a 12-week, open-label study using ProAir Results: Of 360 patients included in the predictive analysis, 64 experienced a total of 78 exacerbations. Increased albuterol use preceded exacerbations; the mean number of inhalations in the 24-hours preceding an exacerbation was 7.3 (standard deviation 17.3). The machine learning model, using gradient-boosting trees with data from the eMDPI and baseline patient characteristics, predicted an impending exacerbation over the following 5 days with an ROC AUC of 0.83 (95% confidence interval: 0.77-0.90). The feature of the model with the highest weight was the mean number of daily inhalations during the 4 days prior to the day the prediction was made. Conclusion: A machine learning model to predict impending asthma exacerbations using data from the eMDPI was successfully developed. This approach may support a shift from reactive care to proactive, preventative, and personalized management of chronic respiratory diseases.
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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.