A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University Hospital

Anders L. Madsen, Kristian G. Olesen, Jørn Munkhof Møller, Nicolaj Søndberg-Jeppesen, Frank Jensen, Thomas Mulvad Larsen, Per Henriksen, Morten Lindblad, Trine Søby Christensen
Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:617-620, 2020.

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 this Paper


BibTeX
@InProceedings{pmlr-v138-madsen20b, 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 M{\o}ller, J{\o}rn Munkhof and S{\o}ndberg-Jeppesen, Nicolaj and Jensen, Frank and Larsen, Thomas Mulvad and Henriksen, Per and Lindblad, Morten and Christensen, Trine S{\o}by}, booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models}, pages = {617--620}, year = {2020}, editor = {Jaeger, Manfred and Nielsen, Thomas Dyhre}, volume = {138}, series = {Proceedings of Machine Learning Research}, month = {23--25 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v138/madsen20b/madsen20b.pdf}, url = {http://proceedings.mlr.press/v138/madsen20b.html}, 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. } }
Endnote
%0 Conference Paper %T A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University Hospital %A Anders L. Madsen %A Kristian G. Olesen %A Jørn Munkhof Møller %A Nicolaj Søndberg-Jeppesen %A Frank Jensen %A Thomas Mulvad Larsen %A Per Henriksen %A Morten Lindblad %A Trine Søby Christensen %B Proceedings of the 10th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2020 %E Manfred Jaeger %E Thomas Dyhre Nielsen %F pmlr-v138-madsen20b %I PMLR %P 617--620 %U http://proceedings.mlr.press/v138/madsen20b.html %V 138 %X 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.
APA
Madsen, A.L., Olesen, K.G., Møller, J.M., Søndberg-Jeppesen, N., Jensen, F., Larsen, T.M., Henriksen, P., Lindblad, M. & Christensen, T.S.. (2020). A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University Hospital. Proceedings of the 10th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 138:617-620 Available from http://proceedings.mlr.press/v138/madsen20b.html.

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