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Pediatr Emerg Care. 2019 Jul 1. doi: 10.1097/PEC.0000000000001858. [Epub ahead of print]

Deep Learning Algorithm to Predict Need for Critical Care in Pediatric Emergency Departments.

Author information

1
From the Department of Emergency Medicine.
2
Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon.
3
Department of Emergency Medicine, Sejong General Hospital, Gyunggi, Korea.

Abstract

BACKGROUND AND OBJECTIVES:

Emergency department (ED) overcrowding is a national crisis in which pediatric patients are often prioritized at lower levels. Because the prediction of prognosis for pediatric patients is important but difficult, we developed and validated a deep learning algorithm to predict the need for critical care in pediatric EDs.

METHODS:

We conducted a retrospective observation cohort study using data from the Korean National Emergency Department Information System, which collected data in real time from 151 EDs. The study subjects were pediatric patients who visited EDs from 2014 to 2016. The data were divided by date into derivation and test data. The primary end point was critical care, and the secondary endpoint was hospitalization. We used age, sex, chief complaint, symptom onset to arrival time, arrival mode, trauma, and vital signs as predicted variables.

RESULTS:

The study subjects consisted of 2,937,078 pediatric patients of which 18,253 were critical care and 375,078 were hospitalizations. For critical care, the area under the receiver operating characteristics curve of the deep learning algorithm was 0.908 (95% confidence interval, 0.903-0.910). This result significantly outperformed that of the pediatric early warning score (0.812 [0.803-0.819]), conventional triage and acuity system (0.782 [0.773-0.790]), random forest (0.881 [0.874-0.890]), and logistic regression (0.851 [0.844-0.858]). For hospitalization, the deep-learning algorithm (0.782 [0.780-0.783]) significantly outperformed the other methods.

CONCLUSIONS:

The deep learning algorithm predicted the critical care and hospitalization of pediatric ED patients more accurately than the conventional early warning score, triage tool, and machine learning methods.

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