A multi-granular stacked regression for forecasting long-term demand in Emergency Departments
Abstract Background In the United Kingdom, Emergency Departments (EDs) are under significant pressure due to an ever-increasing number of attendances. Understanding how the capacity of other urgent care services and the health of a population may influence ED attendances is imperative for commission...
Main Authors: | Charlotte James, Richard Wood, Rachel Denholm |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2023-02-01
|
Series: | BMC Medical Informatics and Decision Making |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12911-023-02109-3 |
Similar Items
-
Forecasting emergency department arrivals using INGARCH models
by: Juan C. Reboredo, et al.
Published: (2023-10-01) -
Mode of Arrival Aware Models for Forecasting Flow of Patient and Length of Stay in Emergency Departments
by: Mustafa Gökalp Ataman, et al.
Published: (2022-03-01) -
FORECASTING PATIENT LENGTH OF STAY IN AN EMERGENCY DEPARTMENT BY ARTIFICIAL NEURAL NETWORKS
by: Muhammet Gül, et al.
Published: (2015-07-01) -
A Comparison of Univariate and Multivariate Forecasting Models Predicting Emergency Department Patient Arrivals during the COVID-19 Pandemic
by: Egbe-Etu Etu, et al.
Published: (2022-06-01) -
Linear regression long-term energy demand forecast modelling in Ogun State, Nigeria
by: O.O. Ade-Ikuesan, et al.
Published: (2019-05-01)