Application of time series model in prediction of nosocomial infection for cancer patients
10.3760/cma.j.cn311365-20200609-00652
- VernacularTitle:时间序列模型在肿瘤患者医院感染发病率预测中的应用
- Author:
Congcong XIA
;
Lijuan WANG
;
Lixia CAI
;
Shujing ZHANG
;
Yuan WANG
;
Yan ZHANG
- From:
Chinese Journal of Infectious Diseases
2021;39(4):199-203
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the applicability of time series model in predicting incidence of nosocomial infection in a cancer center in Shanghai, and to provide the references for early warning and prevention.Methods:The nosocomial infection data of inpatients of a tertiary oncology hospital in Shanghai from 2013 to 2018 were collected. The autoregressive integrated moving average (ARIMA) model and the exponential smoothing model were established by SPSS 22.0 expert modeler. The fitting predictions were compared between these two time series models to select the optimal one. The nosocomial infection data from January 2019 to June 2019 were used to test the predictive effect of the model.Results:A total of 379 477 cancer inpatients were studied, 3 170 of which acquired nosocomial infection and the incidence was 0.84% from 2013 to 2018. Additive Holt-Winters method exponential smoothing model was the better model with R2of 0.82. Using this model, the predicted value fitted well with observed value from January 2019 to June 2019, and the mean relative percentage error was 15.22%. Conclusion:Additive Holt-Winters method exponential smoothing model could be used to fit and predict the tendency of nosocomial infection among cancer patients, which can provide reference for surveillance of nosocomial infection in oncology hospitals.
