Development and Validation of a Bayesian Network Predicting Intubation Following Hospital Arrival Among Injured Children

Abstract

Inadequate airway management can contribute to preventable trauma deaths. Current machine learning tools for predicting intubation in trauma are limited to adult populations and include predictors not readily available at the time of patient arrival. We developed a Bayesian network to predict intubation in injured children and adolescents using observable data available upon or immediately after patient arrival.

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