Predicting hospital emergency department visits with deep learning approaches

• Autorzy: Zhao Xinxing , Lai Joel Weijia , Ho Andrew Fu Wah , Liu Nan , Ong Marcus Eng Hock , Cheong Kang Hao

Identyfikatory

DOI

10.1016/j.bbe.2022.07.008

• Języki publikacji: EN

Abstrakty

EN

Overcrowding in emergency department (ED) causes lengthy waiting times, reduces adequate emergency care and increases rate of mortality. Accurate prediction of daily ED visits and allocating resources in advance is one of the solutions to ED overcrowding problem. In this paper, a deep stacked architecture is being proposed and applied to the daily ED visits prediction problem with deep components such as Long Short Term Memory (LSTM), Gated Recurrent Units (GRU) and simple Recurrent Neural Network (RNN). The proposed architecture achieves very high mean accuracy level (94.28–94.59%) in daily ED visits predictions. We have also compared the performance of this architecture with non-stacked deep models and traditional prediction models. The results indicate that deep stacked models outperform (4–7%) the traditional prediction models and other non-stacked deep learning models (1–2%) in our prediction tasks. The application of deep neural network in ED visits prediction is novel as this is one of the first studies to apply a deep stacked architecture in this field. Importantly, our models have achieved better prediction accuracy (in one case comparable) than the state-of-the-art in the literature.

Słowa kluczowe

EN

deep learning; deep stacked architecture; ED prediction; emergency department; LSTM; RNN; recurrent neural network; GRU; gated recurrent units

PL

uczenie głębokie; oddział ratunkowy; LSTM; RNN; sieć neuronowa rekurencyjna; GRU

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 3
• Strony: 1051--1065
• Opis fizyczny: Bibliogr. 82 poz., rys., tab., wykr.

Twórcy

autor

Zhao Xinxing

• Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore

autor

Lai Joel Weijia

• Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore

autor

Ho Andrew Fu Wah

• Pre-hospital & Emergency Research Centre, Duke-National University of Singapore Medical School, Singapore
• Department of Emergency Medicine, Singapore General Hospital, Singapore

autor

Liu Nan

• Health Services and Systems Research, Duke-NUS Medical School, Singapore

autor

Ong Marcus Eng Hock

• Department of Emergency Medicine, Singapore General Hospital, Singapore
• Health Services and Systems Research, Duke-NUS Medical School, Singapore

autor

Cheong Kang Hao

• Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore

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