A deep learning architecture for forecasting daily emergency department visits with acuity levels

Xinxing Zhao, Kainan Li, Candice Ke En Ang, Andrew Fu Wah Ho, Nan Liu, Marcus Eng Hock Ong and Kang Hao Cheong

Chaos, Solitons & Fractals, 2022, vol. 165, issue P1

Abstract: Accurate forecasting of Emergency Department (ED) visits is important for decision-making purposes in hospitals. It helps to form tactical and operational level plans, which facilitates staff and resource allocations in advance. A dataset recording the daily visits of patients at the ED of a regional hospital over a 3-year period is used in this study. Patients are triaged into 3 acuity levels: P1, P2 and P3, with P1 being patients with severe or life threatening conditions, whereas P3 being patients with minor injuries requiring less urgent attention. A novel deep learning forecasting structure, which has advantages of both Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), is being developed and applied to forecasting daily visits (up to 56 days into the future) for the different acuity levels. The features included in this study are calendar days, public holidays, Pollution Standard Index (PSI) readings, rainfall and daily average temperature. The effectiveness of our newly developed model, in terms of forecasting accuracy, is demonstrated and compared with other deep learning models. Our model achieves mean absolute percentage errors (MAPEs) of 17.37%, 7.19%, 6.11% and 4.50% in forecasting P1, P2, P3 and total visits respectively, and has demonstrated superior performance when evaluated against state-of-the-art studies in the literature. This study illustrates that utilization of our hybrid model comprising LSTM with CNN layers can provide a significant improvement over these existing deep learning models for ED daily visits forecasting.

Keywords: Deep neural networks; Emergency department; Patient visit forecasting; Healthcare; Time series forecasting (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077922009560
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:165:y:2022:i:p1:s0960077922009560

DOI: 10.1016/j.chaos.2022.112777

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().