Prediction of Daily Blood Sampling Room Visits Based on ARIMA and SES Model.

Prediction of Daily Blood Sampling Room Visits Based on ARIMA and SES Model.
Comput Math Methods Med. 2020;2020:1720134
Authors: Zhang X, Yu Y, Xiong F, Luo L
Abstract
This paper is aimed at establishing a combined prediction model to predict the demand for medical care in terms of daily visits in an outpatient blood sampling room, which provides a basis for rational arrangement of human resources and planning. On the basis of analyzing the comprehensive characteristics of the randomness, periodicity, trend, and day-of-the-week effects of the daily number of blood collections in the hospital, we firstly established an autoregressive integrated moving average model (ARIMA) model to capture the periodicity, volatility, and trend, and secondly, we constructed a simple exponential smoothing (SES) model considering the day-of-the-week effect. Finally, a combined prediction model of the residual correction is established based on the prediction results of the two models. The models are applied to data from 60 weeks of daily visits in the outpatient blood sampling room of a large hospital in Chengdu, for forecasting the daily number of blood collections about 1 week ahead. The result shows that the MAPE of the combined model is the smallest overall, of which the improvement during the weekend is obvious, indicating that the prediction error of extreme value is significantly reduced. The ARIMA model can extract the seasonal and nonseasonal c...

Source: Computational and Mathematical Methods in Medicine - September 25, 2020 Category: Statistics Tags: Comput Math Methods Med Source Type: research