基于 LSTM 和 GRU 模型的新疆结膜炎门诊患者预测。
Prediction of outpatients with conjunctivitis in Xinjiang based on LSTM and GRU models.
机构信息
College of Mathematics and System Science, Xinjiang University, Urumqi Xinjiang, China.
Department of Ophthalmology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
出版信息
PLoS One. 2023 Sep 21;18(9):e0290541. doi: 10.1371/journal.pone.0290541. eCollection 2023.
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.
背景
合理、准确地预测门诊量有助于医院管理者优化医疗资源配置，便于精细化医院管理，对于提高医院效率和治疗能力具有重要意义。
方法
基于新疆医科大学第一附属医院眼科 2017 年 1 月 1 日至 2019 年 12 月 31 日的结膜炎门诊数据，构建并评估了长短期记忆（LSTM）和门控循环单元（GRU）模型，用于预测门诊量。
结果
在预测未来 31 天的结膜炎就诊人数时，LSTM 模型的均方根误差（RMSE）为 2.86，平均绝对误差（MAE）为 2.39；GRU 模型的 RMSE 为 2.60，MAE 为 1.99。
结论
GRU 方法可以更好地预测医院门诊流量随时间的趋势，从而为医务人员和门诊管理提供决策支持。
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