Long-term prediction of Iranian blood product supply using LSTM: a 5-year forecast

• Published in: BMC medical informatics and decision making 2024-07, Vol.24 (1), p.213-8, Article 213
• Main Authors: Miri-Moghaddam, Ebrahim, Bizhaem, Saeede Khosravi, Moezzifar, Zohre, Salmani, Fatemeh
• Format: Article
• Language: English
• Subjects: Apheresis; ARIMA; Artificial intelligence; Blood & organ donations; Blood Banks; Blood products; Blood transfusion; Blood Transfusion - statistics & numerical data; Blood transfusions; Consumption; Consumption data; Data mining; Deep learning; Erythrocytes; Forecasting; Forecasts and trends; Goodness of fit; Health care facilities; Humans; Industrialized nations; Iran; Life span; Longitudinal Studies; LSTM; Machine learning; Methods; Neural networks; Neural Networks, Computer; Supply and demand; Time series; Trends
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02614-z

This study aims to predict the trend of procurement and storage of various blood products, as well as planning and monitoring the consumption of blood products in different centers across Iran based on artificial intelligence until the year 2027. This research constitutes a time-series investigation within the realm of longitudinal studies. In this study, information on the number of packed red blood cells (RBC), leukoreduced red blood cells (LR-RBC), and platelets (PLT), PLT-Apheresis, and fresh frozen plasma (FFP) was requested from all blood transfusion centers in the country and extracted using a unified protocol. After the initial examination of the information and addressing data issues and inconsistencies, the corrected data were analyzed. Both conventional and artificial intelligence approaches were used to predict each product in this study. The best model was selected based on goodness-of-fit indicators RMSE and MAPE. Based on the obtained results, the FFP product will follow a relatively consistent process similar to previous years in the next five years. The PLT product is predicted to have a growing trend over the next 5 years, which applies to both the demand and supply of the product. The PLT-Apheresis product also shows a similar upward trend, albeit with a lower growth rate. The RBC product will have a constant trend over a 5-year period (long-term) according to both models, taking into account short-term changes. Similarly, there is a similar trend in LR-RBC, with the expectation that short-term pattern repetition will continue over a 5-year period (long-term). Comparing the goodness-of-fit results, the LSTM model proved to be better for predicting the dominant blood products. The growth of the elderly population and diseases related to old age, and on the other hand, the trend of increasing the consumption of the product with a short lifespan (PLT) requires the activation of the management of the patient's blood, especially in relation to this product in medical centers. The trend for other products in the next five years is similar to previous years, and no growth in demand is observed. The LSTM method, considering periodic and cyclical events, has performed the prediction.