A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University Hospital
Abstract
This paper presents a software system for predicting patient flow at the emergency department of Aalborg University Hospital. The system uses Bayesian networks as the underlying technology for the predictions. A Bayesian network model has been developed for predicting the hourly rate of patients arriving at the emergency department at Aalborg University Hospital. One advantage of using Bayesian networks is that domain knowledge and historical data can easily be combined into an intuitive graphical model. The aim of this paper is to describe the software system delivering the predictions of the Bayesian network model as a decision-support system for employee shift scheduling at the emergency department.
Cite
Text
Madsen et al. "A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University Hospital." Proceedings of pgm 2020, 2020.Markdown
[Madsen et al. "A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University Hospital." Proceedings of pgm 2020, 2020.](https://mlanthology.org/pgm/2020/madsen2020pgm-software/)BibTeX
@inproceedings{madsen2020pgm-software,
title = {{A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University Hospital}},
author = {Madsen, Anders L. and Olesen, Kristian G. and Møller, Jørn Munkhof and Søndberg-Jeppesen, Nicolaj and Jensen, Frank and Larsen, Thomas Mulvad and Henriksen, Per and Lindblad, Morten and Christensen, Trine Søby},
booktitle = {Proceedings of pgm 2020},
year = {2020},
pages = {617-620},
volume = {138},
url = {https://mlanthology.org/pgm/2020/madsen2020pgm-software/}
}