Development of a low-dimensional model to predict admissions from triage at a pediatric emergency department.

ABSTRACT:

Objectives

This study aims to develop and internally validate a low-dimensional model to predict outcomes (admission or discharge) using commonly entered data up to the post-triage process to improve patient flow in the pediatric emergency department (ED). In hospital settings where electronic data are limited, a low-dimensional model with fewer variables may be easier to implement.

Methods

This prognostic study included ED attendances in 2017 and 2018. The Cross Industry Standard Process for Data Mining methodology was followed. Eligibility criteria was applied to the data set, splitting into 70% train and 30% test. Sampling techniques were compared. Gradient boosting machine (GBM), logistic regression, and naïve Bayes models were created. Variables of importance were obtai

SUBMITTER: Leonard F 

PROVIDER: S-EPMC9286530 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature