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Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review
Tanvi Ojha1,2, Atushi Patel1, Krishihan Sivapragasam1
1Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada.
Insights
Machine learning (ML) models show promise in predicting pediatric asthma outcomes, with logistic regression and random forests being common. Future research should improve data quality and model interpretability for better clinical application in childhood asthma management.
Area of Science:
- Pediatric Pulmonology
- Computational Health
- Biomedical Informatics
Background:
- Machine learning (ML) offers a novel approach to predicting asthma-related outcomes in children.
- This integration is crucial for advancing pediatric healthcare and asthma management strategies.
Purpose of the Study:
- To conduct a scoping review of studies published since 2019 on ML algorithms for pediatric asthma.
- To analyze the applications and predictive performance of these ML models.
- To identify trends, common algorithms, and key outcomes in ML-driven pediatric asthma research.
Main Methods:
- A comprehensive literature search was conducted across major databases (Ovid MEDLINE, Embase, Cochrane Library, CINAHL, Web of Science) from January 2019 to July 2023.
- Studies involving ML models for predicting asthma outcomes in children (<18 years) were included.
- Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool.
Main Results:
- Fifteen studies met the inclusion criteria, utilizing ML techniques like logistic regression (47%) and random forests (40%).
- Key applications included predicting exacerbations (XGBoost, AUROC 0.76), classifying phenotypes (SVM, AUROC 0.79), diagnosis (ANN, AUROC 0.63), and identifying risk factors (Random Forests, AUROC 0.88).
- Most studies had low to moderate risk of bias, but limitations included data quality, sample size, and interpretability.
Conclusions:
- ML demonstrates diverse applications in predicting pediatric asthma outcomes, with varying model strengths.
- XGBoost, SVM, ANN, and Random Forests show significant predictive capabilities for different asthma-related outcomes.
- Future research must prioritize data quality, larger sample sizes, and enhanced model interpretability for clinical translation.
Background:
The integration of machine learning (ML) in predicting asthma-related outcomes in children presents a novel approach in pediatric health care.
Objective:
This scoping review aims to analyze studies published since 2019, focusing on ML algorithms, their applications, and predictive performances.
Methods:
We searched Ovid MEDLINE ALL and Embase on Ovid, the Cochrane Library (Wiley), CINAHL (EBSCO), and Web of Science (core collection). The search covered the period from January 1, 2019, to July 18, 2023. Studies applying ML models in predicting asthma-related outcomes in children aged <18 years were included. Covidence was used for citation management, and the risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool.
Results:
From 1231 initial articles, 15 met our inclusion criteria. The sample size ranged from 74 to 87,413 patients. Most studies used multiple ML techniques, with logistic regression (n=7, 47%) and random forests (n=6, 40%) being the most common. Key outcomes included predicting asthma exacerbations, classifying asthma phenotypes, predicting asthma diagnoses, and identifying potential risk factors. For predicting exacerbations, recurrent neural networks and XGBoost showed high performance, with XGBoost achieving an area under the receiver operating characteristic curve (AUROC) of 0.76. In classifying asthma phenotypes, support vector machines were highly effective, achieving an AUROC of 0.79. For diagnosis prediction, artificial neural networks outperformed logistic regression, with an AUROC of 0.63. To identify risk factors focused on symptom severity and lung function, random forests achieved an AUROC of 0.88. Sound-based studies distinguished wheezing from nonwheezing and asthmatic from normal coughs. The risk of bias assessment revealed that most studies (n=8, 53%) exhibited low to moderate risk, ensuring a reasonable level of confidence in the findings. Common limitations across studies included data quality issues, sample size constraints, and interpretability concerns.
Conclusions:
This review highlights the diverse application of ML in predicting pediatric asthma outcomes, with each model offering unique strengths and challenges. Future research should address data quality, increase sample sizes, and enhance model interpretability to optimize ML utility in clinical settings for pediatric asthma management.
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