ArticlePediatric pulmonology2019
Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma.
Article in Pediatric pulmonology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Data pipeline quality: development and validation of a quality assessment tool for data-driven algorithms and artificial intelligence in healthcare.BMJ health & care informatics · 2026Article
- Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review.JMIR AI · 2024Article
- Initial emergency department vital signs may predict PICU admission in pediatric patients presenting with asthma exacerbation.The Journal of asthma : official journal of the Association for the Care of Asthma · 2023Observational
- Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children.Pediatric research · 2023Review
- State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review.JMIR medical informatics · 2022Review
- Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.BMJ open respiratory research · 2022Article
- Computer-Aided Detection of Respiratory Sounds in Bronchial Asthma Patients Based on Machine Learning Method.Sovremennye tekhnologii v meditsine · 2022Article
- Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.Frontiers in pediatrics · 2021Article
- Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease.International journal of medical sciences · 2021Review
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Authors and funding
6 authors.
Funding
Abstract
objectivesManual clinical scoring systems are the current standard used for acute asthma clinical care pathways. No automated system exists that assesses disease severity, time course, and treatment impact in pediatric acute severe asthma exacerbations. WORKING HYPOTHESIS: machine learning applied to continuous vital sign data could provide a novel pediatric-automated asthma respiratory score (pARS) by using the manual pediatric asthma score (PAS) as the clinical care standard.
methodsContinuous vital sign monitoring data (heart rate, respiratory rate, and pulse oximetry) were merged with the health record data including a provider-determined PAS in children between 2 and 18 years of age admitted to the pediatric intensive care unit (PICU) for status asthmaticus. A cascaded artificial neural network (ANN) was applied to create an automated respiratory score and validated by two approaches. The ANN was compared with the Normal and Poisson regression models.
resultsOut of an initial group of 186 patients, 128 patients met inclusion criteria. Merging physiologic data with clinical data yielded >37 000 data points for model training. The pARS score had good predictive accuracy, with 80% of the pARS values within ±2 points of the provider-determined PAS, especially over the mid-range of PASs (6-9). The Poisson and Normal distribution regressions yielded a smaller overall median absolute error.
conclusionsThe pARS reproduced the manually recorded PAS. Once validated and studied prospectively as a tool for research and for physician decision support, this methodology can be implemented in the PICU to objectively guide treatment decisions.
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