Machine learning-based prediction of critical illness in children visiting the emergency department.

ABSTRACT:

Objectives

Triage is an essential emergency department (ED) process designed to provide timely management depending on acuity and severity; however, the process may be inconsistent with clinical and hospitalization outcomes. Therefore, studies have attempted to augment this process with machine learning models, showing advantages in predicting critical conditions and hospitalization outcomes. The aim of this study was to utilize nationwide registry data to develop a machine learning-based classification model to predict the clinical course of pediatric ED visits.

Methods

This cross-sectional observational study used data from the National Emergency Department Information System on emergency visits of children under 15 years of age from January 1, 2016, to December 31, 2017.

SUBMITTER: Hwang S 

PROVIDER: S-EPMC8853514 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature