Recurrent neural network prediction on clinical usage of red blood cells

Abstract

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, 1 621.5 U) and 2.5% in May 2021 (the highest level, 2 397.0 U). Conclusion The artificial intelligence RNN model can predict clinical RBCs consumption well under major public health emergencies.

Keywords

• prediction of clinical consumption of red blood cells
• artificial intelligence
• recurrent neural network(rnn)