What question did this study set out to answer?

To explore machine learning classification algorithms for predicting hospital admission of infants treated by EMS after a suspected BRUE.

February 11, 2026

Use of Machine Learning to Predict Hospital Admission for EMS-Treated Infants After a Suspected BRUE

Key Points

To explore machine learning classification algorithms for predicting hospital admission of infants treated by EMS after a suspected BRUE.
Data from a pediatric care system for infants suspected of BRUE were collected.
An 80%/20% split for training and testing data was performed.
A random forest model identified key variables influencing hospital admission.
Multiple machine learning models were trained and evaluated for predictive performance.
Out of 508 infants, 300 (59%) were admitted to the hospital, with 76 (15%) needing critical care.
The support vector machine model achieved the highest AUROC of 0.85, with sensitivity of 0.88 and specificity of 0.71.
Other models, including extreme gradient boosting, random forest, and logistic regression, had similar AUROC results (0.83 to 0.84).

Abstract

Objectives: This study explored the use of different applied machine learning (ML) classification algorithms to predict hospital admission for infants treated by emergency medical services (EMS) after a suspected brief resolved unexplained event (BRUE). Methods: Data from a large regionalized pediatric care system were obtained for infants in which paramedic suspected a BRUE and who were transported between July 2017 and February 2021. After data pre-processing, a random 80%/20% split for training and testing was performed. First, a random forest ML classification model was used to identify and select the most important variables influencing the prediction of hospital admission. Then, multiple ML-based models and a statistical model were trained with this subset of variables and evaluated the performance of each to predict hospital admission. Model performance characteristics including the area under the receiver operator curve (AUROC) were reported. Results: A total of 508 infants were included; 300 (59%) were admitted and 76 (15%) required critical care. The most important variables in predicting hospital admission were age, history of bystander interventions (ie, cardiopulmonary resuscitation and back blows), presence of past medical history, and a normal appearing examination. In the prediction of hospital admission, the support vector machine model achieved the highest AUROC of 0.85, with a sensitivity of 0.88 (95% CI: 0.80-0.96) and specificity of 0.71 (95% CI: 0.57-0.85). The predictive performance of the extreme gradient boosting, RF, and logistic regression models were similar (AUROC: 0.83 to 0.84). Conclusions: The applied ML models demonstrated good predictive performance for hospital admission for EMS-treated infants with a paramedic suspected BRUE. ML and statistical models had similar predictive performance.

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