基于循环神经网络模型预测临床红细胞用量

Recurrent neural network prediction on clinical usage of red blood cells

目的探讨重大突发公共卫生事件背景下的临床红细胞用量的预测方法,为给血站血液采集工作及血液库存管理提供科学决策依据。方法采用人工智能方法中的循环神经网络(recurrent nerual network,RNN)模型对宜昌市2001—2017年临床血液各型红细胞用量进行分析和建模,并对2019—2021年36个月的临床红细胞用量进行科学预测。结果RNN网络模型预测性能较好,A、B、O、AB型红细胞预测值均方根误差RMSE分别为156.7、133.4、204.2、51.3,平均相对误差MRE分别为6.4%、6.6%、8.5%、7.1%。模型对第一轮新冠疫情期间(2020年1—6月)的红细胞用量变化趋势有较好的预判性,预测到了2020年2月份的用量最低点及随后的恢复增长,对第二轮新冠疫情期间(2021年1—6月)的红细胞用量的预测精度较高,如A型红细胞用量在2021年2月(用量低点1621.5 U)和5月(用量高点2397.0 U)时RNN模型预测相对误差分别为5.2%和2.5%。结论人工智能循环神经网络模型能较好地预测,重大突发公共卫生事件背景下的临床红细胞用量。

Objective To explore the prediction of clinical red blood cells(RBCs)consumption under major public health emergencies,so as to provide scientific basis for blood collection and blood inventory management.Methods The clinical consumption of different types of RBCs in Yichang from 2001 to 2017 was analyzed and modeled using the recurrent neural network(RNN)model,and the clinical RBCs consumption between January 2019 and December 2021(36 months)were scientifically predicted.Results The RNN model showed good prediction performance.The root mean square errors(RMSE)of RNN prediction values of A,B,O,AB type of RBCs were 156.7,133.4,204.2 and 51.3,respectively,with the average relative errors(MRE)at 6.4%,6.6%,8.5%and 7.1%,respectively.The model predicted the changing trend of RBCs consumption during the first round of COVID-19 outbreak(January to June,2020)and forecasted the lowest level of consumption in February 2020 and a subsequent recovery in growth.The prediction of RBCs consumption during the second round of COVID-19 pandemic(January to June,2021)was of high accuracy.For example,the relative errors of RNN models for A type RBCs consumption were 5.2%in Feb 2021(the lowest level,1621.5 U)and 2.5%in May 2021(the highest level,2397.0 U).Conclusion The artificial intelligence RNN model can predict clinical RBCs consumption well under major public health emergencies.