his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Patient visit forecasting in an emergency department using a deep neural network approach
Department of Production and Transportation Engineering, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
Department of Mechanical Engineering, Islamic Azad University, Roudehen Branch, Roudehen, Iran.
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Production and Automation Engineering)ORCID iD: 0000-0001-5530-3517
Department of Production and Transportation Engineering, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
2019 (English)In: Kybernetes, ISSN 0368-492X, E-ISSN 1758-7883Article in journal (Refereed) Epub ahead of print
Abstract [en]

This study aims to investigate factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to 7 days.In this study, first the important factors to influence the demand in EDs were extracted from literature then the relevant factors to our study are selected. Then a deep neural network is applied for constructing a reliable predictor.Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable through employing the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression (MLR), autoregressive integrated moving average (ARIMA), support vector regression (SVR), generalized linear models (GLM), generalized estimating equations (GEE), seasonal ARIMA (SARIMA) and combined ARIMA and linear regression (LR) (ARIMA-LR).We applied this study in a single ED to forecast the patient visits. Applying the same method in different EDs may give us a better understanding of the performance of the model. The same approach can be applied in any other demand forecasting after some minor modifications.To the best of our knowledge, this is the first study to propose the use of long short-term memory (LSTM) for constructing a predictor of the number of patient visits in EDs.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2019.
Keywords [en]
Patient Visit Forecasting, Deep Neural Networks, Long Short-term Memory, Emergency Department
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-17635DOI: 10.1108/K-10-2018-0520OAI: oai:DiVA.org:his-17635DiVA, id: diva2:1348180
Available from: 2019-09-03 Created: 2019-09-03 Last updated: 2019-11-06Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Fathi, Masood

Search in DiVA

By author/editor
Fathi, Masood
By organisation
School of Engineering ScienceThe Virtual Systems Research Centre
In the same journal
Kybernetes
Production Engineering, Human Work Science and Ergonomics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 351 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf