Machine Learning and Initial Nursing Assessment-Based Triage System for Emergency Department.

ABSTRACT:

Objectives

The aim of this study was to develop machine learning (ML) and initial nursing assessment (INA)-based emergency department (ED) triage to predict adverse clinical outcome.

Methods

The retrospective study included ED visits between January 2016 and December 2017 that resulted in either intensive care unit admission or emergency room death. We trained four classifiers using logistic regression and a deep learning model on INA and low dimensional (LD) INA, logistic regression on the Korea Triage and acuity scale (KTAS) and Sequential Related Organ Failure Assessment (SOFA). We varied the outcome ratio for external validation. Finally, variables of importance were identified using the random forest model's information gain. The four most influential variables were use

SUBMITTER: Yu JY 

PROVIDER: S-EPMC7010940 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature