Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

Key Points

Question

Do machine learning approaches improve the ability to predict clinical outcomes and disposition of children at emergency department triage?

Findings

In this prognostic study of a nationally representative sample of 52 037 emergency department visits by children, machine learning–based triage models had better discrimination ability for clinical outcomes and disposition compared with the conventional triage approaches, with a higher sensitivity for the critical care outcome and higher specificity for the hospitalization outcome.

Meaning

Machine learning may improve the prediction ability of triage approaches and could be used to reduce undertriage of critically ill children and to improve resource allocation in emergency departments.

Abstract

This prognostic study uses data from the National Hospital Ambulatory Medical Care Survey to test the ability of 4 machine learning approaches to predict clinical outcomes of children presenting to emergency department triage.

Abstract

Importance

While machine learning approaches may enhance prediction ability, little is known about their utility in emergency department (ED) triage.

Objectives

To examine the performance of machine learning approaches to predict clinical outcomes and disposition in children in the ED and to compare their performance with conventional triage approaches.

Design, Setting, and Participants

Prognostic study of ED data from the National Hospital Ambulatory Medical Care Survey from January 1, 2007, through December 31, 2015. A nationally representative sample of 52 037 children aged 18 years or younger who presented to the ED were included. Data analysis was performed in August 2018.

Main Outcomes and Measures

The outcomes were critical care (admission to an intensive care unit and/or in-hospital death) and hospitalization (direct hospital admission or transfer). In the training set (70% random sample), using routinely available triage data as predictors (eg, demographic characteristics and vital signs), we derived 4 machine learning–based models: lasso regression, random forest, gradient-boosted decision tree, and deep neural network. In the test set (the remaining 30% of the sample), we measured the models’ prediction performance by computing C statistics, prospective prediction results, and decision curves. These machine learning models were built for each outcome and compared with the reference model using the conventional triage classification information.

Results

Of 52 037 eligible ED visits by children (median [interquartile range] age, 6 [2-14] years; 24 929 [48.0%] female), 163 (0.3%) had the critical care outcome and 2352 (4.5%) had the hospitalization outcome. For the critical care prediction, all machine learning approaches had higher discriminative ability compared with the reference model, although the difference was not statistically significant (eg, C statistics of 0.85 [95% CI, 0.78-0.92] for the deep neural network vs 0.78 [95% CI, 0.71-0.85] for the reference; P = .16), and lower number of undertriaged critically ill children in the conventional triage levels 3 to 5 (urgent to nonurgent). For the hospitalization prediction, all machine learning approaches had significantly higher discrimination ability (eg, C statistic, 0.80 [95% CI, 0.78-0.81] for the deep neural network vs 0.73 [95% CI, 0.71-0.75] for the reference; P < .001) and fewer overtriaged children who did not require inpatient management in the conventional triage levels 1 to 3 (immediate to urgent). The decision curve analysis demonstrated a greater net benefit of machine learning models over ranges of clinical thresholds.

Conclusions and Relevance

Machine learning–based triage had better discrimination ability to predict clinical outcomes and disposition, with reduction in undertriaging critically ill children and overtriaging children who are less ill.