ArticleApplied clinical informatics2013
Comparing predictions made by a prediction model, clinical score, and physicians: pediatric asthma exacerbations in the emergency department.
Article in Applied clinical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 4 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial intelligence-based models in predicting acute exacerbations and diagnosing pediatric asthma: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2024Pooled it
- Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.Journal of medical systems · 2024Pooled it
- Article
- Multimodal EHR-Based Prediction of Pediatric Asthma Exacerbations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- AI model for predicting asthma prognosis in children.The journal of allergy and clinical immunology. Global · 2025Article
- Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset.Scientific reports · 2025Article
- Assessing the Impact of New Technologies on Managing Chronic Respiratory Diseases.Journal of clinical medicine · 2024Review
- Comparing Clinician Estimates versus a Statistical Tool for Predicting Risk of Death within 45 Days of Admission for Cancer Patients.Applied clinical informatics · 2024Article
- Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.medRxiv : the preprint server for health sciences · 2024Article
- Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review.Advances in therapy · 2024Review
- Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.Scientific reports · 2023Observational
- Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study.Journal of medical Internet research · 2023Article
- Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.BMJ open respiratory research · 2022Article
- Clinician Accuracy in Identifying and Predicting Organ Dysfunction in Critically Ill Children.Critical care medicine · 2020Article
- Applications of machine learning in acute care research.Journal of the American College of Emergency Physicians open · 2020Article
- An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning.Journal of medical systems · 2019Article
- Machine Learning-Based Prediction of Clinical Outcomes for Children During Emergency Department Triage.JAMA network open · 2019Article
- Applications of Machine Learning Approaches in Emergency Medicine; a Review Article.Archives of academic emergency medicine · 2019Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAsthma exacerbations are one of the most common medical reasons for children to be brought to the hospital emergency department (ED). Various prediction models have been proposed to support diagnosis of exacerbations and evaluation of their severity.
objectivesFirst, to evaluate prediction models constructed from data using machine learning techniques and to select the best performing model. Second, to compare predictions from the selected model with predictions from the Pediatric Respiratory Assessment Measure (PRAM) score, and predictions made by ED physicians.
designA two-phase study conducted in the ED of an academic pediatric hospital. In phase 1 data collected prospectively using paper forms was used to construct and evaluate five prediction models, and the best performing model was selected. In phase 2 data collected prospectively using a mobile system was used to compare the predictions of the selected prediction model with those from PRAM and ED physicians. MEASUREMENTS: Area under the receiver operating characteristic curve and accuracy in phase 1; accuracy, sensitivity, specificity, positive and negative predictive values in phase 2.
resultsIn phase 1 prediction models were derived from a data set of 240 patients and evaluated using 10-fold cross validation. A naive Bayes (NB) model demonstrated the best performance and it was selected for phase 2. Evaluation in phase 2 was conducted on data from 82 patients. Predictions made by the NB model were less accurate than the PRAM score and physicians (accuracy of 70.7%, 73.2% and 78.0% respectively), however, according to McNemar's test it is not possible to conclude that the differences between predictions are statistically significant.
conclusionBoth the PRAM score and the NB model were less accurate than physicians. The NB model can handle incomplete patient data and as such may complement the PRAM score. However, it requires further research to improve its accuracy.
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