ArticleJMIR medical informatics2020
Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: 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 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- 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
- Towards Intelligent Assessment in Personalized Physiotherapy with Computer Vision.Sensors (Basel, Switzerland) · 2025Article
- A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection.JMIR medical informatics · 2024Article
- Primary Care Asthma Attack Prediction Models for Adults: A Systematic Review of Reported Methodologies and Outcomes.Journal of asthma and allergy · 2024Review
- Article
- Development of a risk prediction model to predict the risk of hospitalization due to exacerbated asthma among adult asthma patients in a lower middle-income country.BMC pulmonary medicine · 2023Article
- Error and Timeliness Analysis for Using Machine Learning to Predict Asthma Hospital Visits: Retrospective Cohort Study.JMIR medical informatics · 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
- Ranking Rule-Based Automatic Explanations for Machine Learning Predictions on Asthma Hospital Encounters in Patients With Asthma: Retrospective Cohort Study.JMIR medical informatics · 2021Article
- A Roadmap for Automating Lineage Tracing to Aid Automatically Explaining Machine Learning Predictions for Clinical Decision Support.JMIR medical informatics · 2021Article
- Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis.JMIR research protocols · 2021Article
- Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study.Journal of medical Internet research · 2021Article
- Generalizability of an Automatic Explanation Method for Machine Learning Prediction Results on Asthma-Related Hospital Visits in Patients With Asthma: Quantitative Analysis.Journal of medical Internet research · 2021Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAsthma causes numerous hospital encounters annually, including emergency department visits and hospitalizations. To improve patient outcomes and reduce the number of these encounters, predictive models are widely used to prospectively pinpoint high-risk patients with asthma for preventive care via care management. However, previous models do not have adequate accuracy to achieve this goal well. Adopting the modeling guideline for checking extensive candidate features, we recently constructed a machine learning model on Intermountain Healthcare data to predict asthma-related hospital encounters in patients with asthma. Although this model is more accurate than the previous models, whether our modeling guideline is generalizable to other health care systems remains unknown.
objectiveThis study aims to assess the generalizability of our modeling guideline to Kaiser Permanente Southern California (KPSC).
methodsThe patient cohort included a random sample of 70.00% (397,858/568,369) of patients with asthma who were enrolled in a KPSC health plan for any duration between 2015 and 2018. We produced a machine learning model via a secondary analysis of 987,506 KPSC data instances from 2012 to 2017 and by checking 337 candidate features to project asthma-related hospital encounters in the following 12-month period in patients with asthma.
resultsOur model reached an area under the receiver operating characteristic curve of 0.820. When the cutoff point for binary classification was placed at the top 10.00% (20,474/204,744) of patients with asthma having the largest predicted risk, our model achieved an accuracy of 90.08% (184,435/204,744), a sensitivity of 51.90% (2259/4353), and a specificity of 90.91% (182,176/200,391).
conclusionsOur modeling guideline exhibited acceptable generalizability to KPSC and resulted in a model that is more accurate than those formerly built by others. After further enhancement, our model could be used to guide asthma care management. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.5039.
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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.