Table 2.
Summary of the included studies on MLa applications in pediatric asthma: predictors, clinical outcomes, and models.
| Study | Potential predictors, variables of interests-grouped | Metrics | Data source | Outcomes | ML models |
| AlSaad et al [30], 2022 | Demographic data, medication use, health service use, clinical parameters and characteristics (comorbid illnesses), and insurance information | AUROCb (0.85), AUCc-PRd (0.74), and F1-score (0.61) | EHRse | Frequency of EDf use (number of visits made by pediatric patients during a 1-year predication window) | Deep learning: recurrent neural networks |
| Bhardwaj et al [31], 2023 | Demographic data (age and weight) and clinical parameters and characteristics (C-reactive protein, eosinophilic granulocytes, oxygen saturation, premedication inhaled corticosteroid+long-acting β-2 agonist, other premedication, Pulmicort or celestamine during hospitalization, and azithromycin during hospitalization) | SVMg differentiated between allergic and nonallergic asthma most well: accuracy (77.8%), precision (0.81), true positive rate (0.73), true negative rate (0.81), F1-score (0.81), and AUROC (0.79); because of the imbalance between both groups, a stratified 10-fold cross-validation was used | EHRs | Classify predominantly allergic asthma and nonallergic asthma among preschool children | RFsh, extreme gradient boosting, SVMs, adaptive boosting, extra tree classifier, and logistic regression |
| Bose et al [32], 2021 | Demographic data (race, sex, ethnicity, and language spoken), geographic location (state of residency at the time of their first asthma diagnosis), insurance information (Medicaid enrollment), care site information (place of service such as EDs or office visits and provider specialties at first asthma diagnosis), medical hxi (age of first and last asthma diagnoses and nonasthma-related clinical visits) | Mean ANSA, median ANSA, precision, recall, F1-score, and accuracy; XGBoost presented the best mean ANSAj: mean ANSA (0.43), median ANSA (0.43), precision (0.95), recall (0.82), F1-score (0.88), and accuracy (0.81) | EHRs | Occurrence of asthma diagnosis by the age of 10 years following an asthma incident | Naive Bayes, K-nearest neighbors, logistic regression, RFs, and XGBoost |
| Deliu et al [33], 2020 | Medical hx and medication use (asthma diagnosis, use of asthma medication, current wheeze, asthma severity, and lung function) and risk factors (environmental tobacco smoke, pet ownership, length of breastfeeding, day-care attendance, presence of older siblings, and family hx of asthma) | FVCk, FEV1l, IEm, FEn (early-onset frequent exacerbations), IE (93.7%), and FE (6.3%); shorter duration of breastfeeding was the strongest risk factor. FEV1/FVC of FE group: 85.1% at 8 years old | EHRs and health surveys | Examine risk factors that result in asthma-related outcomes in late childhood | K-means clustering |
| Deng et al [34], 2021 | Demographic data (sex, race, age, and grade), family hx (job status, health status and hx, and education), insurance information, and risk factors (home conditions, such as carpet in house, tile flooring, or home location and year, animal triggers, home-related ventilators, and school characteristics) | Percentage and PR; top contributing factors: asthma, family rhinitis hx (relative importance: 10.40%), plant pollen trigger (relative importance: 5.48%), and bedroom carpet (relative importance: 3.58%). Allergy-related symptoms: plant pollen trigger (relative importance: 10.88%), higher paternal education (relative importance: 7.33%), and bedroom carpet (relative importance: 5.28%) | Health surveys | Evaluating factors in indoor environments (home vs school) contributing to asthma and allergy-related symptoms | RFs and decision tree |
| Gorham et al [35], 2023 | Demographic data (age, sex, and race) and medical hx and medication use (inhaled or oral steroid prescribed, ED visits in a year, moderate to severe asthma, and asthma-related primary care visits in a year) | AUROC; internal validation: 0.769. 10-fold cross-validation AUROC: 0.737 | EHRs | ED visit because of asthma exacerbations (also known as AERo); asthma exacerbations: asthma-related emergency | Logistic regression |
| Habukawa et al [36], 2020 | Audio features (wheeze sounds: frequency, intensity, and duration) and demographic data (age) | Sensitivity, specificity, PPVp, and NPVq; sensitivity (100%), specificity (95.7%), PPV (90.3%), and NPV (100%) | EHRs | Identification of wheeze sounds vs nonwheeze sounds | Decision tree |
| Hee et al [37], 2019 | Demographic data (age, sex, race, and weight), clinical parameters and characteristics (temperature, respiratory rate, heart rate, and shortness of breath), audio features (cough sounds: mel-frequency cepstral coefficients and constant-Q cepstral coefficients), and medical hx (asthma, allergic rhinitis, and recurrent wheeze) | Sensitivity (82.81%) and specificity (84.76%) | EHRs and health surveys | Classify and differentiate asthmatic coughs from normal voluntary coughs | Gaussian mixture model-universal background model |
| Hogan et al [38], 2022 | Demographic data (sex and age), insurance, family hx (family member with alcohol or drug issues, hx of abuse, housing instability, and foster care), clinical parameters and characteristics (LOSr, admission season, and chronic conditions), and hospital characteristics (hospital ownership, teaching status, and hospital size) | AUC; logistic regression (0.592) and ANNss (0.637) | Claims data and biomedical databases | Asthma hospital readmission 180 days after hospital discharge | Logistic regression and ANNs |
| Hurst et al [39], 2022 | Demographic data (age and sex), medical hx and medication use (comorbidities and prescribed asthma control plan), insurance, and health care use (inpatient admissions, ambulatory visits, and ED) | AUC at day 30, 90, and 180; LASSOt (0.753, 0.740, and 0.732), RFs (0.757, 0.747, and 0.729), and XGBoost (0.761, 0.752, and 0.739) | EHRs and biomedical databases | Predict the occurrence of asthma exacerbation; asthma exacerbation: any encounter with an asthma-related ICD-9 or -10u code and a prescription for a systemic steroid | LASSO, RFs, and XGBoost |
| Krautenbacher et al [40], 2019 | Clinical parameters and characteristics (genes, including PKN2v, PTK2w, and ALPPx, and breastfeeding), and demographic data (age and sex) | AUC; boosting was the best model for all data sets: 0.81 | Health surveys and biomedical databases | Distinguish between healthy children, those with mild to moderate allergic asthma, and those with nonallergic asthma | LASSO, elastic net, RFs, and stochastic gradient boosting |
| Messinger et al [41], 2019 | Demographic data (age, sex, and race) and medication use, medical hx, and medications (LOS, PASy including vital sign data such as heart rate, respiratory rate, oxygen saturation, respiratory support, and medications) | Median absolute error; balanced set MAEz: 1.21 | EHRs and biomedical databases | Use of vital sign data to predict the presence of asthma and to generate a novel pediatric-automated asthma score | ANNs |
| Seol et al [42], 2020 | Demographic data (age, sex, ethnicity, and weight), family hx (asthma and smoking during pregnancy), medical hx (diagnosis of asthma, eczema, allergic rhinitis, eosinophilia, total IgEaa, asthma and associated outcomes such as persistent asthma, pertussis, pneumonia), and health care use (visits per year) | Percentage; NLPab-PACac+/NLP–APIad+: 1614 (20%), NLP-PAC+ only: 954 (12%), NLP-API+ only: 105 (1%), and NLP-PAC–/NLP-API–: 5523 (67%); NLP-PAC) and NLP-API); asthmatic children classified as NLP-PAC+/NLP-API+ showed earlier onset asthma, more Th2ae-high profile, poorer lung function, higher asthma exacerbation, and higher risk of asthma-associated comorbidities compared with other groups | EHRs | Identifying characteristics that will identify childhood asthma and its subgroups using 2 algorithms | NLP |
| Seol et al [25], 2021 | Medical hx and medications (IgE count, eosinophil count, smoking exposure, hx of allergic rhinitis, previous exacerbations, asthma diagnosis, and medication use) and demographic data (age, sex, and race) | IQR and P value; asthma exacerbation: intervention 12%, control 15%, P=.60; Time (min) taken by the clinician to take a clinical decision, median: intervention 3.5 min vs control 11.3 min | EHRs | Determine the presence of asthma exacerbation to reduce its frequency using clinical information; asthma exacerbation: ED visit, hospitalization, or outpatient visit requiring systemic corticosteroids for asthma | NLP |
| Sills et al [43], 2021 | Demographic data (age, race, and sex), insurance, medical hx, and medications (ED and treatment factors: time to triage, time to first medication and asthma medication, ED hourly volume, and disposition including admitted or discharged) | AUC, accuracy, and F1-; model 1: triage (RF-AUC 0.831, accuracy 0.777, and F1-score 0.635, and logistic regression-AUC 0.795, accuracy 0.731, and F1-score 0.564); model 2: 60 minutes after patients’ arrival (RF-AUC 0.886, accuracy 0.795, and F1-score 0.689, and logistic regression-AUC 0.823, accuracy 0.753, and F1-score 0.618) | EHRs | Predict the need for hospitalization of pediatric patients with asthma | RFs and logistic regression |
aML: machine learning.
bAUROC: area under the receiver operating characteristic curve.
cAUC: area under cover.
dPR: precision recall.
eEHR: electronic health record.
fED: emergency department.
gSVM: support vector machine.
hRF: random forest.
ihx: history.
jANSA: average negative predictive value specificity area.
kFVC: forced vital capacity.
lFEV1: forced expiratory volume in the first second.
mIE: infrequent exacerbation.
nFE: frequent exacerbation.
oAER: asthma emergency risk.
pPPV: positive predictive value.
qNPV: negative predictive value.
rLOS: length of stay.
sANN: artificial neural network.
tLASSO: least absolute shrinkage and selection operator.
uICD-9 or -10: International Classification of Diseases, 9th or 10th Revisions.
vPKN2: protein kinase N2.
wPTK2: protein tyrosine kinase 2.
xALPP: alkaline phosphatase, placental.
yPAS: pediatric asthma score.
zMAE: masked autoencoder.
aaIgE: immunoglobulin E.
abNLP: natural language processing.
acPAC: predetermined asthma criteria.
adAPI: Asthma Predictive Index.
aeTh2: T helper 2 cells.