Prediction for the outpatient amount of childhood common respiratory diseases based on multivariate LSTM model with lag effect

QIN Jia-jie; CHEN Cong; GUAN Jing; LIU Wei

doi:10.16462/j.cnki.zhjbkz.2022.09.012

Volume 26 Issue 9

Sep. 2022

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2022 > 26(9): 1057-1064

QIN Jia-jie, CHEN Cong, GUAN Jing, LIU Wei. Prediction for the outpatient amount of childhood common respiratory diseases based on multivariate LSTM model with lag effect[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2022, 26(9): 1057-1064. doi: 10.16462/j.cnki.zhjbkz.2022.09.012

Citation:

QIN Jia-jie, CHEN Cong, GUAN Jing, LIU Wei. Prediction for the outpatient amount of childhood common respiratory diseases based on multivariate LSTM model with lag effect[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2022, 26(9): 1057-1064. doi: 10.16462/j.cnki.zhjbkz.2022.09.012

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Prediction for the outpatient amount of childhood common respiratory diseases based on multivariate LSTM model with lag effect

doi: 10.16462/j.cnki.zhjbkz.2022.09.012

QIN Jia-jie¹,
CHEN Cong¹,
GUAN Jing^{1
,
,},
LIU Wei^{2
,
,}

1.
School of Mathematics, Tianjin University, Tianjin 300350, China
2.
Children's Hospital of Tianjin University, Tianjin Children's Hospital, Tianjin 300134, China

Funds:

National Natural Science Foundation of China 72174138

National Natural Science Foundation of Tianjin, China 20JCZDJC00660

More Information

Corresponding author: GUAN Jing, E-mail: guanjing@tju.edu.cn; LIU Wei, E-mail: Lance1971@163.com
Received Date: 2021-12-31
Rev Recd Date: 2022-04-27

Available Online: 2022-09-17

Publish Date: 2022-09-10

Abstract

Abstract

Objective To construct the prediction model of the daily outpatient amount of childhood common respiratory diseases and analyze the trend of the outpatient amount in the future, which will provide data support for the scientific prevention and control of common respiratory diseases in children. Methods Based on the daily outpatient cases of a hospital and meteorological and air pollutant data from January 1, 2017 to December 31 2019, the distributed lag nonlinear model (DLNM) was used to analyze the influence and lag effect of average daily temperature and pollutant concentration on the daily outpatient amount in spring and autumn semesters.A multivariate long and short-term memory (LSTM) model was constructed to predict daily outpatient amounts in the spring and autumn semesters. Results The median average daily temperature in the spring and autumn semesters was selected for research, and we found that the impact of average daily temperature on the daily outpatient amount in the autumn semester lagged 7 days and lasted for about 10 days, while the effect on the spring semester was immediate and lasted for about 4 days.The multivariate LSTM model combined with the lag effect can predict daily outpatient amount in spring and autumn semesters well, the mean absolute percentage error (MAPE) on the test set was 4.59% and 4.77%, respectively. Conclusion The multivariate LSTM model combined with the lag effect can accurately predict the daily outpatient amount, which provides a scientific basis for the prevention and control of diseases.
- Distributed lag nonlinear model,
- Multivariate LSTM model,
- Childhood common respiratory diseases

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References(14)

References

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