ORIGINAL RESEARCH article
Front. Med.
Sec. Hematology
This article is part of the Research TopicMathematical Approaches in Advancing Medical Science, Physical Fitness, and Clinical SciencesView all 12 articles
Time Series Forecasting of Red Blood Cell Demand in Hematology Patients Using SARIMA and Exponential Smoothing Models: A Retrospective Analysis in a Chinese Tertiary Hospital
Provisionally accepted
- 1Zigong First People's Hospital, Zigong, China
- 2The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
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Background: Red blood cells (RBCs) infusion is very important for the treatment of hematology patients, but how to maintain a balanced state between the supply and demand of RBCs is still a major challenge. Objective: This study aimed to explore the feasibility of seasonal autoregressive integrated moving average (SARIMA) model and exponential smoothing (ES) model in predicting the clinical demand of RBCs for hematology patients each month. Methods: Our study collected the monthly RBCs usage data of hematology patients from January 2014 to December 2023 to establish the SARIMA model and ES model respectively. Then, the optimal model was used to forecast the monthly usage of RBCs from January to June 2024, and we subsequently compared the data with actual values to evaluate the prediction effect of the model. Results: The best fitting SARIMA model was SARIMA(2, 1, 0)(1, 1, 1)12, whose R2 = 0.603, MAE = 37.092, MAPE = 13.693, BIC = 7.896. The best fitting ES model was Winters addition model, whose R2 = 0.702, MAE = 32.617, MAPE = 12.138, BIC = 7.485. The mean relative errors of two models were 0.085 and 0.159, respectively. The SARIMA(2,1,0)(1,1,1)12 model performed better in prediction. Conclusions: Compared with the ES model, the SARIMA model has a smaller mean relative error in predicting RBCs usage in hematology patients. DM test also verify this result. But in the future, more similar research data are needed to make research more convincing.
Keywords: SARIMA model, Es model, Red blood cells usage, Model prediction, Hematology patients
Received: 24 Feb 2025; Accepted: 07 Nov 2025.
Copyright: © 2025 Liu, Cai, Cai and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jusong Liu, 864735628@qq.com
Xuemei Xu, xmxu1023@163.com
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