Table 1.

Application of ML^a models in pediatric asthma management through predictive and diagnostic modalities.

Category	Outcome	Primary ML models
Prediction of asthma exacerbations [25,30,35,38,39,43]	Any encounter (outpatients, EDb visits, and hospitalization) with an asthma-related ICD-9 or ICD-10c code or a prescription for a systemic steroid	Neural networks, LASSOd regression, RFse, XGBoost, and natural language processing
Classification of asthma phenotypes [31,40,42]	Allergic vs nonallergic asthma and mild vs moderate-severe asthma	SVMsf and stochastic gradient boosting
Asthma diagnosis prediction [32,41]	Prediction of asthma diagnosis and PASg	XGBoost, ANNsh, and natural language processing
Identification of potential risk factors for asthma [33,34]	Potential risk factors (such as family hxi, medical hx, and environmental triggers) for asthma-related outcomes (including symptom severity and lung function)	K-means clustering, RFs, and decision tree
Sound-based asthma or wheezing diagnosis [36,37]	Identification of wheezing vs nonwheezing sounds and differentiation between asthmatic and normal coughs	Decision trees and Gaussian mixture models

^aML: machine learning.

^bED: emergency department.

^cICD-9 or ICD-10: International Classification of Diseases, 9th or 10th revisions.

^dLASSO: least absolute shrinkage and selection operator.

^eRF: random forest.

^fSVM: support vector machine.

^gPAS: pediatric asthma score.

^hANN: artificial neural network.

ⁱhx: history.