Comparative analysis of machine learning approaches for predicting frequent emergency department visits.

Journal: Health informatics journal

Published Date: Jan 1, 2022

Abstract

BACKGROUND: Emergency Department (ED) overcrowding is an emerging risk to patient safety. This study aims to assess and compare the predictive ability of machine learning (ML) models for predicting frequent ED users.

Authors

Razieh Safaripour

Department of Community Health and Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada.
Hyun Ja June Lim

Department of Community Health and Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada.

Keywords

Area Under Curve Emergency Service, Hospital Humans Logistic Models Machine Learning Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (35686745)

Comparative analysis of machine learning approaches for predicting frequent emergency department visits.

Abstract

Authors

Keywords

External Resources

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