Forecasting emergency department overcrowding: A deep learning framework
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DOI: 10.1016/j.chaos.2020.110247
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Cited by:
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- 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).
- 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.
- 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.
- 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).
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Keywords
Emergency departments; Patient flows; ED demands; Forecasting; Deep learning;All these keywords.
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