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1. 万达信息股份有限公司,上海 201112
2. 复旦大学附属妇产科医院,上海 200090
3. 长春理工大学,吉林 长春 130022
Online First:2019-11,
Published:15 November 2019
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Pengfei LU, Chengjie XU, Jingyi ZHANG, et al. Research on the prediction of outpatient volume based on SARIMA-LSTM[J]. Big Data Research, 2019, 5(6): 2019053-1.
Pengfei LU, Chengjie XU, Jingyi ZHANG, et al. Research on the prediction of outpatient volume based on SARIMA-LSTM[J]. Big Data Research, 2019, 5(6): 2019053-1. DOI: 10.11959/j.issn.2096-0271.2019053.
为了实现更加稳健和精准的门诊量预测,构建了一种基于SARIMA-LSTM的门诊量预测模型。该方法首先使用SARIMA模型对门诊量进行单指标建模,提取门诊量指标蕴含的周期、趋势等信息,然后构建了以节日天数、法定上班天数、平均最高气温等多个相关指标为输入的多对一LSTM模型,对SARIMA模型残差进行进一步学习,实现残差与多个变量间的非线性关系抽取。实证结果表明,构建SARIMA-LSTM混合模型相较5种主流预测方法具有更高的一步预测精度,具有较好的实际应用价值。
In order to achieve more robust and accurate outpatient volume prediction
a hybrid prediction model based on SARIMALSTM was constructed.SARIMA model was used to build a single index model of outpatient volume to extract the cycle
trend and other information contained in outpatient volume index.Then multiple related indexes
including holiday days
legal working days
average maximum temperature
were used as input of a many-to-one LSTM model
in order to further learn the residual of SARIMA model and extract the nonlinear relationship between residual and multiple variables.The empirical results show that the SARIMA-LSTM hybrid model constructed in this paper has higher prediction accuracy than the five mainstream prediction methods
so it has good practical application value.
YIN S F , WU J H , WANG D , et al . Predicting of the outpatient based on grey model [J ] . Lecture Notes in Electrical Engineering , 2014 , 270 : 383 - 388 .
LI X , MA C , LEI H , et al . Applications of SARIMA model in forecasting outpatient amount [J ] . Chinese Medical Record English Edition , 2013 , 1 ( 3 ): 124 - 128 .
WANG X L , CHEN J , SHI T X , et al . Application of winters exponential additive model in outpatient visits prediction [J ] . Chinese Journal of Health Informatics and Management , 2016 , 13 ( 2 ): 214 - 216 .
LI Y M , WU F , ZHENG C , et al . Predictive analysis of outpatient volumes of a first-class grade a general hospital through ARIMA models [J ] . Chinese Medical Record English Edition , 2014 , 2 ( 8 ): 364 - 367 .
LI L , WANG Z , ZHANG X L . The accuracy of monthly outpatient volume prediction based on LSTM deep neural network [J ] . China Digital Medicine , 2019 , 14 ( 1 ): 14 - 17 .
HUANG D , WU Z H . Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization [J ] . Plos One , 2017 , 12 ( 2 ):e0172539.
SANG F W , WEI Z , CHEN H , et al . Short-term hospital outpatient amount forecasting based on similar days and extreme learning machine [J ] . China Digital Medicine , 2018 , 13 ( 2 ): 113 - 115 .
ZHANG Y L , YANG Z S . Grey RBF neural network based forecasting of outpatient capacity in modern hospital [J ] . Computer Engineering & Applications , 2010 , 46 ( 29 ): 225 - 228 .
GERS F A , SCHMIDHUBER J A , CUMMINS F A . Learning to forget:continual prediction with LSTM [J ] . Neural Computation , 2000 , 12 ( 10 ): 2451 - 2471 .
CLEVELAND R B , CLEVELAND W S . STL:a seasonal-trend decomposition procedure based on loess [J ] . Journal of Official Statistics , 1990 , 6 ( 1 ): 3 - 33 .
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