ArticleJMIR medical informatics2020
Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 73 citations in OpenAlex.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Machine learning for prediction of asthma exacerbations among asthmatic patients: a systematic review and meta-analysis.BMC pulmonary medicine · 2023Pooled it
- 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
- Leveraging artificial intelligence for the management of preschool wheeze: A narrative review.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025Review
- Exhale-Dx™: A non-invasive, real-time breath analysis system using deep learning for asthma diagnosis.Journal of biological methods · 2025Article
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection.JMIR medical informatics · 2024Article
- Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in Pediatrics.Journal of medical Internet research · 2024Article
- Primary Care Asthma Attack Prediction Models for Adults: A Systematic Review of Reported Methodologies and Outcomes.Journal of asthma and allergy · 2024Review
- Article
- Prediction of short-acting beta-agonist usage in patients with asthma using temporal-convolutional neural networks.JAMIA open · 2023Article
- Article
- Error and Timeliness Analysis for Using Machine Learning to Predict Asthma Hospital Visits: Retrospective Cohort Study.JMIR medical informatics · 2022Article
- A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research-A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee.The journal of allergy and clinical immunology. In practice · 2022Article
- Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models.BMC medical informatics and decision making · 2022Article
- A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma.JMIR medical informatics · 2022Article
- Predicting Continuity of Asthma Care Using a Machine Learning Model: Retrospective Cohort Study.International journal of environmental research and public health · 2022Article
- Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study.Journal of medical Internet research · 2022Article
- Machine learning: A modern approach to pediatric asthma.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2022Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundAs a major chronic disease, asthma causes many emergency department (ED) visits and hospitalizations each year. Predictive modeling is a key technology to prospectively identify high-risk asthmatic patients and enroll them in care management for preventive care to reduce future hospital encounters, including inpatient stays and ED visits. However, existing models for predicting hospital encounters in asthmatic patients are inaccurate. Usually, they miss over half of the patients who will incur future hospital encounters and incorrectly classify many others who will not. This makes it difficult to match the limited resources of care management to the patients who will incur future hospital encounters, increasing health care costs and degrading patient outcomes.
objectiveThe goal of this study was to develop a more accurate model for predicting hospital encounters in asthmatic patients.
methodsSecondary analysis of 334,564 data instances from Intermountain Healthcare from 2005 to 2018 was conducted to build a machine learning classification model to predict the hospital encounters for asthma in the following year in asthmatic patients. The patient cohort included all asthmatic patients who resided in Utah or Idaho and visited Intermountain Healthcare facilities during 2005 to 2018. A total of 235 candidate features were considered for model building.
resultsThe model achieved an area under the receiver operating characteristic curve of 0.859 (95% CI 0.846-0.871). When the cutoff threshold for conducting binary classification was set at the top 10.00% (1926/19,256) of asthmatic patients with the highest predicted risk, the model reached an accuracy of 90.31% (17,391/19,256; 95% CI 89.86-90.70), a sensitivity of 53.7% (436/812; 95% CI 50.12-57.18), and a specificity of 91.93% (16,955/18,444; 95% CI 91.54-92.31). To steer future research on this topic, we pinpointed several potential improvements to our model.
conclusionsOur model improves the state of the art for predicting hospital encounters for asthma in asthmatic patients. After further refinement, the model could be integrated into a decision support tool to guide asthma care management allocation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.5039.
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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.