Evidence map›Paper›PMID 39075453›Full record

ArticleBMC medical informatics and decision making2024

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

Ebrahim Miri-Moghaddam, Saeede Khosravi Bizhaem, Zohre Moezzifar, Fatemeh Salmani

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Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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2 citing papers in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Ebrahim Miri-MoghaddamBlood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine, Tehran, Iran.
Saeede Khosravi BizhaemCardiovascular Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran.
Zohre MoezzifarBlood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine, Tehran, Iran.
Fatemeh SalmaniDepartment of Epidemiology and Biostatistics, School of Health, Cardiovascular Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran. Fatemehsalmani1364@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis 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.

methodsThis 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.

resultsBased 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.

conclusionsThe 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.

Indexed as

ForecastingBlood BanksBlood TransfusionHumansIranLongitudinal StudiesNeural Networks, ComputerARIMABlood productsDeep learningLSTM

Identifiers

PMID39075453
PMCPMC11288095

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.