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

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Ebrahim Miri-Moghaddam, Saeede Khosravi Bizhaem, Zohre Moezzifar, Fatemeh Salmani
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Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

利用 LSTM 对伊朗血液制品供应进行长期预测:5 年预测。
研究背景本研究旨在基于人工智能预测伊朗各地不同中心直至 2027 年各种血液制品的采购和储存趋势,以及规划和监测血液制品的消耗情况:本研究是纵向研究领域中的一项时间序列调查。在这项研究中,我们要求全国所有输血中心提供包装红细胞(RBC)、白细胞还原红细胞(LR-RBC)、血小板(PLT)、PLT-Apheresis 和新鲜冰冻血浆(FFP)的数量信息,并使用统一的协议进行提取。在对信息进行初步检查并处理数据问题和不一致之处后,对修正后的数据进行分析。本研究采用传统方法和人工智能方法对每种产品进行预测。根据拟合优度指标 RMSE 和 MAPE,选出了最佳模型:根据所获得的结果,未来五年,FFP 产品将遵循与往年类似的相对一致的进程。预测 PLT 产品在未来 5 年内将呈增长趋势,这既适用于该产品的需求,也适用于该产品的供应。PLT-Apheresis 产品也呈现出类似的上升趋势,尽管增长率较低。根据这两个模型,考虑到短期变化,RBC 产品在 5 年内(长期)的趋势将保持不变。同样,LR-RBC 也有类似的趋势,预计短期模式重复将持续 5 年(长期)。比较拟合度结果,事实证明 LSTM 模型在预测主要血液制品方面更胜一筹:一方面,老年人口的增长和与老年相关疾病的增加,另一方面,寿命短的血液制品(PLT)的消耗量呈上升趋势,这就要求激活对患者血液的管理,尤其是医疗中心对该产品的管理。其他产品在未来五年的趋势与前几年相似,需求没有增长。考虑到周期性和循环性事件,采用 LSTM 方法进行了预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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