基于SARIMA-LSTM的门诊量预测研究

卢鹏飞; 须成杰; 张敬谊; 韩侣; 李静

doi:10.11959/j.issn.2096-0271.2019053

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基于SARIMA-LSTM的门诊量预测研究

专题：大数据整理 | 更新时间：2024-06-03

- 基于SARIMA-LSTM的门诊量预测研究
- Research on the prediction of outpatient volume based on SARIMA-LSTM
- 大数据 2019年5卷第6期页码：2019053-1
- 作者机构：
  
  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
- 作者简介：
  
  [ "卢鹏飞（1991- ），男，万达信息股份有限公司大数据产品部数据挖掘工程师，主要研究方向为医疗数据挖掘、机器学习、光谱分析" ]
  [ "须成杰（1983- ），男，复旦大学附属妇产科医院信息科工程师，主要研究方向为大数据及人工智能、互联网医疗+物联网、医院信息无纸化管理、医疗可信云计算等" ]
  [ "张敬谊（1974- ），女，博士，万达信息股份有限公司大数据产品部总经理、教授级高级工程师，主要研究方向为并行计算、智能分析、城市信息化等" ]
  [ "韩侣（1992- ），男，长春理工大学硕士生，主要研究方向为统计机器学习、数据挖掘" ]
  [ "李静（1973- ），女，万达信息股份有限公司大数据产品部资深产品经理、高级工程师，主要研究方向为医疗卫生大数据、健康医疗大数据+人工智能" ]
- 基金信息：
  
  上海市科委民生科技支撑计划专项临床医学科技创新项目;Special Clinical Medical Science and Technology Innovation Project of Livelihood Science and Technology Support Plan of Shanghai Science and Technology Commission(17411950500);上海市科委民生科技支撑计划专项临床医学科技创新项目;Special Clinical Medical Science and Technology Innovation Project of Livelihood Science and Technology Support Plan of Shanghai Science and Technology Commission(17411950505)
- DOI：10.11959/j.issn.2096-0271.2019053
  中图分类号： TP183
- 网络首发：2019-11，
  
  纸质出版：2019-11-15
- 稿件说明：
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卢鹏飞, 须成杰, 张敬谊, 等. 基于SARIMA-LSTM的门诊量预测研究[J]. 大数据, 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.
卢鹏飞, 须成杰, 张敬谊, 等. 基于SARIMA-LSTM的门诊量预测研究[J]. 大数据, 2019,5(6):2019053-1. DOI： 10.11959/j.issn.2096-0271.2019053.

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.

摘要

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。

Abstract

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.

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Keywords

references

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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