Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques.

ABSTRACT:

Background

The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health needs. Accurate ED revisit prediction could aid early intervention and ensure efficient healthcare resource allocation. We examine the potential increased accuracy and performance of graph neural network (GNN) machine learning models compared to recurrent neural network (RNN), and baseline conventional machine learning and regression models for predicting ED revisit in electronic health record (EHR) data.

Methods

This study used EHR data for children and youth aged 4-17 seeking services at McMaster Children's Hospital's Child and Youth Men

SUBMITTER: Saggu S 

PROVIDER: S-EPMC10854017 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature