基于LSTM深度神经网络的月门诊量预测精度研究
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摘要
目的:分析新疆地区慢性阻塞性肺病的月门诊量变化趋势,对医院月门诊量预测方法进行探讨,为医院合理配置医疗资源和提高救助能力提供科学依据。方法:采用ARIMA模型和LSTM模型对新疆地区慢性阻塞性肺病的月门诊量的时间序列进行预测,使用RMSPE值评价不同方法的预测精度。结果:ARIMA模型、时间步为1的LSTM、时间步为12的LSTM的RMSPE值分别是20.23%、22.23%和20.01%,相较之下时间步为12的LSTM网络的预测效果较好,时间步为1的LSTM效果最差。结论:LSTM预测医院月门诊量的准确率较高,为医院月门诊量预测提供了新的方法。
Objective: The variation trend of monthly outpatient volume of chronic obstructive pulmonary disease(COPD) in XinJiang was analyzed. The forecasting method of monthly outpatient volume of hospital was discussed to provide scientific basis for rational allocation of medical resources and improvement of rescue capability of hospital. Methods: ARIMA model and LSTM model were used to predict the time series of monthly outpatient volume of COPD in XinJiang and RMSPE value was used to evaluate the prediction accuracy. Results: RMSPE values of ARIMA models, LSTM network with time step 1, LSTM network with time step 12 were 20.23%, 22.23% and 20.01% respectively, compared with the predicted effect of LSTM network with time step 12, and the effect of LSTM with time step 1 was the worst. Conclusion: The accuracy of LSTM in predicting hospital outpatient volume is higher, which provides a new method for predicting hospital outpatient volume.
Objective: The variation trend of monthly outpatient volume of chronic obstructive pulmonary disease(COPD) in XinJiang was analyzed. The forecasting method of monthly outpatient volume of hospital was discussed to provide scientific basis for rational allocation of medical resources and improvement of rescue capability of hospital. Methods: ARIMA model and LSTM model were used to predict the time series of monthly outpatient volume of COPD in XinJiang and RMSPE value was used to evaluate the prediction accuracy. Results: RMSPE values of ARIMA models, LSTM network with time step 1, LSTM network with time step 12 were 20.23%, 22.23% and 20.01% respectively, compared with the predicted effect of LSTM network with time step 12, and the effect of LSTM with time step 1 was the worst. Conclusion: The accuracy of LSTM in predicting hospital outpatient volume is higher, which provides a new method for predicting hospital outpatient volume.
引文
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[2]耿娟.ARIMA模型在医院门诊量预测中的应用[J].中国卫生统计,2014,31(4):643-5.
[3]桑发文,韦哲,陈辉,等.基于相似日和极限学习机方法的医院门诊量短期预测[J].中国数字医学,2018,13(2):113-5.
[4]纪伟伟.基于ARIMA模型肿瘤门诊预测与管理对策研究[D].天津:天津医科大学,2017.
[5]DUAN Y,LV Y,WANG F-Y.Travel time prediction with LSTM neural network;proceedings of the Intelligent Transportation Systems(ITSC)[C],2016 IEEE 19th International Conference on,2016.
[6]FILONOV P,LAVRENTYEV A,VORONTSOV A.Multivariate industrial time series with cyber-attack simulation:Fault detection using an lstm-based predictive data model[J].arXiv preprint arXiv:161206676.
[7]ZHANG R,ZOU Q.Time Series Prediction and Anomaly Detection of Light Curve Using LSTM Neural Network[J].proceedings of the Journal of Physics:Conference Series,2018.
[8]高扬,张媛.慢性阻塞性肺疾病(COPD)主要风险因素的研究进展[J].世界最新医学信息文摘,2016,16(8):31-3.
[9]丁宁,王胜.慢性阻塞性肺疾病发病机制最新研究进展[J].临床肺科杂志,2016,(1):133-6.
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