Mixed time series approaches for forecasting the daily number of hospital blood collections

Abstract

Purpose: Provide new methods to predict the number of hospital blood collections. Methods:The registered outpatients and blood collection patients in a large hospital in China in the period from March 2018 to April 2019 were enrolled in the study. Firstly, we analyzed the time series characteristics of the daily blood collection patients and their correlation with the number of daily outpatients. Then, we used the time series ARIMA and linear regression methods to build the periodic trend model of the blood collections number prediction and the regression prediction model with the number of registered outpatients as an independent variable. Finally, we built a combined prediction model considering mixed time series to predict the number of blood collections in the hospital. Results:The combined prediction model has a higher accuracy and can better explore the characteristics of the number of blood collections compared with other models.It can also give some suggestions for a reasonable blood collection management. Conclusion:The combined prediction model of mixed time series can reflect the change in the blood collections number due to the influence of internal and external factors and can realize the blood collection prediction with a higher accuracy providing a new method for the prediction of the blood collections number.

A Systematic Literature Review of the Blood Supply Chain through Bibliometric Analysis and Taxonomy

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Statistical machine learning models for prediction of China’s maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model

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