A validation of machine learning models for the identification of critically ill children presenting to the paediatric emergency room of a tertiary hospital in South Africa: A proof of concept.

ABSTRACT:

Background

Machine learning (ML) refers to computational algorithms designed to learn from patterns in data to provide insights or predictions related to that data.

Objectives

Multiple studies report the development of predictive models for triage or identification of critically ill children. In this study, we validate machine learning models developed in South Africa for the identification of critically ill children presenting to a tertiary hospital.

Results

The validation sample comprised 267 patients. The event rate for the study outcome was 0.12. All models demonstrated good discrimination but weak calibration. Artificial neural network 1 (ANN1) had the highest area under the receiver operating characteristic curve (AUROC) with a value of 0.84. ANN2 had the highes

SUBMITTER: Pienaar MA 

PROVIDER: S-EPMC11792591 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature