医学信息2026，Vol.39Issue(14)：45-49,5.DOI:10.3969/j.issn.1006-1959.2026.14.007
基于GWO-LSTM的医院日门诊量预测研究
Research on Daily Outpatient Volume Prediction Based on GWO-LSTM
王进军1
作者信息
- 1. 山东省立第三医院智慧医院发展部,山东 济南 250031
- 折叠
摘要
Abstract
This study begins with a statistical analysis of factors influencing outpatient volume to form a multi-dimensional dataset.It then examines the global search capability of the grey wolf optimization(GWO)algorithm and the ability of long short-term memory(LSTM)networks to capture long-term dependencies in sequences.Building on these analyses,a hybrid prediction model integrating GWO and LSTM(GWO-LSTM)is proposed to enhance the accuracy and stability of hospital outpatient volume forecasting.The experimental phase validates the model ' s effectiveness in capturing non-linear features and learning long-term dependencies.Comparative analysis with traditional LSTM models further confirms its potential for improvement in prediction accuracy and robustness.关键词
灰狼优化算法/长短期记忆网络/门诊量预测Key words
Grey wolf optimizer/Long short-term memory network/Outpatient volume prediction分类
医药卫生引用本文复制引用
王进军..基于GWO-LSTM的医院日门诊量预测研究[J].医学信息,2026,39(14):45-49,5.
