摘要
医院门诊量本质上是一种具有潜在规律的时间序列,通过对门诊量进行有效分析和预测,可以更加科学、合理地配置医疗资源。针对门诊量波动幅度较大的时间序列预测问题,提出SIDW-SSA-LSTM模型。首先,通过标幺化反距离加权(SIDW)插值修正原始数据,提高了门诊量数据集的可靠性;然后,采用在时序问题处理上具有良好性能的长短期记忆(LSTM)神经网络,并通过寻优能力强、稳定性好的麻雀搜索算法(SSA)对LSTM网络超参数进行优化,得到SIDW-SSA-LSTM模型。对比实验证明,本文提出的方法可以更加精准地对门诊量进行预测和分析,为医院更好地运营管理提供了重要依据和决策支持。
Hospital outpatient volume is essentially a time series with potential laws.Through effective analysis and prediction of outpatient volume,medical resources can be more scientifically and reasonably allocated.The SIDW-SSA-LSTM model is proposed to predict the time series with large fluctuation of outpatient volume.First,the reliability of the outpatient volume data set is improved by modifying the original data with standardized inverse distance weighting(SIDW)interpolation.Then,the long and short term memory(LSTM)neural network with good performance in time series problem processing is adopted,and the super parameters of LSTM network are optimized by Sparrow Search Algorithm(SSA)with strong optimization ability and good stability,and the SIDW-SSA-LSTM model is obtained.Finally,through comparative experiments,the proposed method can more accurately predict and analyze the outpatient volume,providing an important basis and decision support for better operation and management of the hospital.
作者
樊冲
FAN Chong(Jinzhou Big Data Management Center,Jinzhou Liaoning 121000,China)
出处
《智能计算机与应用》
2023年第12期165-169,共5页
Intelligent Computer and Applications
关键词
门诊量
麻雀搜索算法
LSTM
标幺化反距离加权
outpatient volume
Sparrow search algorithm
LSTM
standardized inverse distance weighting
