IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v139y2020ics0960077920306433.html
   My bibliography  Save this article

Forecasting emergency department overcrowding: A deep learning framework

Author

Listed:
  • Harrou, Fouzi
  • Dairi, Abdelkader
  • Kadri, Farid
  • Sun, Ying

Abstract

As the demand for medical cares has considerably expanded, the issue of managing patient flow in hospitals and especially in emergency departments (EDs) is certainly a key issue to be carefully mitigated. This can lead to overcrowding and the degradation of the quality of the provided medical services. Thus, the accurate modeling and forecasting of ED visits are critical for efficiently managing the overcrowding problems and enable appropriate optimization of the available resources. This paper proposed an effective method to forecast daily and hourly visits at an ED using Variational AutoEncoder (VAE) algorithm. Indeed, the VAE model as a deep learning-based model has gained special attention in features extraction and modeling due to its distribution-free assumptions and superior nonlinear approximation. Two types of forecasting were conducted: one- and multi-step-ahead forecasting. To the best of our knowledge, this is the first time that the VAE is investigated to improve forecasting of patient arrivals time-series data. Data sets from the pediatric emergency department at Lille regional hospital center, France, are employed to evaluate the forecasting performance of the introduced method. The VAE model was evaluated and compared with seven methods namely Recurrent Neural Network (RNN), Long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), Convolutional LSTM Network (ConvLSTM), restricted Boltzmann machine (RBM), Gated recurrent units (GRUs), and convolutional neural network (CNN). The results clearly show the promising performance of these deep learning models in forecasting ED visits and emphasize the better performance of the VAE in comparison to the other models.

Suggested Citation

  • Harrou, Fouzi & Dairi, Abdelkader & Kadri, Farid & Sun, Ying, 2020. "Forecasting emergency department overcrowding: A deep learning framework," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
  • Handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s0960077920306433
    DOI: 10.1016/j.chaos.2020.110247
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920306433
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2020.110247?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Laura McLay & Maria Mayorga, 2010. "Evaluating emergency medical service performance measures," Health Care Management Science, Springer, vol. 13(2), pages 124-136, June.
    2. Jaime González & Juan-Carlos Ferrer & Alejandro Cataldo & Luis Rojas, 2019. "A proactive transfer policy for critical patient flow management," Health Care Management Science, Springer, vol. 22(2), pages 287-303, June.
    3. Sofia Benbelkacem & Farid Kadri & Baghdad Atmani & Sondès Chaabane, 2019. "Machine Learning for Emergency Department Management," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global, vol. 11(3), pages 19-36, July.
    4. Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. Guo, Zhifeng & Zhou, Kaile & Zhang, Xiaoling & Yang, Shanlin, 2018. "A deep learning model for short-term power load and probability density forecasting," Energy, Elsevier, vol. 160(C), pages 1186-1200.
    6. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    7. Swapnarekha, H. & Behera, Himansu Sekhar & Nayak, Janmenjoy & Naik, Bighnaraj, 2020. "Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    8. Rania Boujemaa & Aida Jebali & Sondes Hammami & Angel Ruiz & Hanen Bouchriha, 2018. "A stochastic approach for designing two-tiered emergency medical service systems," Flexible Services and Manufacturing Journal, Springer, vol. 30(1), pages 123-152, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mahmoud Ragab & Faris Kateb & Mohammed W. Al-Rabia & Diaa Hamed & Turki Althaqafi & Abdullah S. AL-Malaise AL-Ghamdi, 2023. "A Machine Learning Approach for Monitoring and Classifying Healthcare Data—A Case of Emergency Department of KSA Hospitals," IJERPH, MDPI, vol. 20(6), pages 1-17, March.
    2. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    3. Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
    4. Eduardo Silva & Margarida F. Pereira & Joana T. Vieira & João Ferreira‐Coimbra & Mariana Henriques & Nuno F. Rodrigues, 2023. "Predicting hospital emergency department visits accurately: A systematic review," International Journal of Health Planning and Management, Wiley Blackwell, vol. 38(4), pages 904-917, July.
    5. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Ho, Andrew Fu Wah & Liu, Nan & Ong, Marcus Eng Hock & Cheong, Kang Hao, 2022. "A deep learning architecture for forecasting daily emergency department visits with acuity levels," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    2. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    3. Rafael Becerra-Vicario & David Alaminos & Eva Aranda & Manuel A. Fernández-Gámez, 2020. "Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry," Sustainability, MDPI, vol. 12(12), pages 1-15, June.
    4. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    5. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
    6. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    7. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    8. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    9. Suyuan Luo & Tsan-Ming Choi, 2024. "Great partners: how deep learning and blockchain help improve business operations together," Annals of Operations Research, Springer, vol. 339(1), pages 53-78, August.
    10. Christopher Gerling & Stefan Lessmann, 2023. "Multimodal Document Analytics for Banking Process Automation," Papers 2307.11845, arXiv.org, revised Nov 2023.
    11. Shree Krishna Acharya & Young-Min Wi & Jaehee Lee, 2019. "Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation," Energies, MDPI, vol. 12(18), pages 1-19, September.
    12. Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    13. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    14. Huang, Shirley Hsueh-Li & Hu, Guo-Hsin & Hsu, Ming-Fu, 2025. "Identifying contextual content-based risk drivers for advanced risk management strategies," Research in International Business and Finance, Elsevier, vol. 73(PB).
    15. Degel, Dirk & Wiesche, Lara & Rachuba, Sebastian & Werners, Brigitte, 2014. "Reorganizing an existing volunteer fire station network in Germany," Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 149-157.
    16. Das, Ronnie & Ahmed, Wasim & Sharma, Kshitij & Hardey, Mariann & Dwivedi, Yogesh K. & Zhang, Ziqi & Apostolidis, Chrysostomos & Filieri, Raffaele, 2024. "Towards the development of an explainable e-commerce fake review index: An attribute analytics approach," European Journal of Operational Research, Elsevier, vol. 317(2), pages 382-400.
    17. Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022. "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers 2203.03342, arXiv.org, revised Nov 2022.
    18. Sigrist, Fabio & Leuenberger, Nicola, 2023. "Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1390-1406.
    19. Xiaojin Xie & Kangyang Luo & Zhixiang Yin & Guoqiang Wang, 2021. "Nonlinear Combinational Dynamic Transmission Rate Model and Its Application in Global COVID-19 Epidemic Prediction and Analysis," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
    20. Rogelio A. Mancisidor & Kjersti Aas, 2022. "Multimodal Generative Models for Bankruptcy Prediction Using Textual Data," Papers 2211.08405, arXiv.org, revised Feb 2024.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s0960077920306433. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.