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Prediction of outpatients with conjunctivitis in Xinjiang based on LSTM and GRU models

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  • Yijia Wang
  • Xianglong Yi
  • Mei Luo
  • Zhe Wang
  • Long Qin
  • Xijian Hu
  • Kai Wang

Abstract

Background: Reasonable and accurate forecasting of outpatient visits helps hospital managers optimize the allocation of medical resources, facilitates fine hospital management, and is of great significance in improving hospital efficiency and treatment capacity. Methods: Based on conjunctivitis outpatient data from the First Affiliated Hospital of Xinjiang Medical University Ophthalmology from 2017/1/1 to 2019/12/31, this paper built and evaluated Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for outpatient visits prediction. Results: In predicting the number of conjunctivitis visits over the next 31 days, the LSTM model had a root mean square error (RMSE) of 2.86 and a mean absolute error (MAE) of 2.39, the GRU model has an RMSE of 2.60 and an MAE of 1.99. Conclusions: The GRU method can better predict trends in hospital outpatient flow over time, thus providing decision support for medical staff and outpatient management.

Suggested Citation

  • Yijia Wang & Xianglong Yi & Mei Luo & Zhe Wang & Long Qin & Xijian Hu & Kai Wang, 2023. "Prediction of outpatients with conjunctivitis in Xinjiang based on LSTM and GRU models," PLOS ONE, Public Library of Science, vol. 18(9), pages 1-10, September.
    • Handle: RePEc:plo:pone00:0290541
    DOI: 10.1371/journal.pone.0290541
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    2. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2020. "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
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