A hybrid neural network based model for blood donation forecasting

To develop a hybrid neural network-based blood donation prediction method, via this predictive model, we can obtain the best estimate of whole blood in Beijing Tongzhou District Central Blood Station and help managers smoothly solve the allocation problem under fluctuating hospital demand and limited resources.

Inspired by the practical problems faced by blood stations providing transfusion services to several hospitals, a hybrid model based on a time-series prediction method and neural network, SARIMAX-TCN-LSTM is proposed for the prediction of daily whole blood donations. The experiment was performed at the central blood station in Tongzhou district, where we used whole blood donations from January 1, 2015, to November 14, 2021, as the subject, supplemented by meteorological and epidemic factors affecting blood donation, to predict daily blood donations for the next two weeks.

The hybrid model significantly outperformed the traditional time series forecasting method on multiple regression metrics, with twice as effective fitting as the baseline and a 33% reduction in Root Mean Squared Error (RMSE). Results indicate that the proposed model can improve the prediction accuracy of daily blood donations, and the co-validity of the structure was evidenced in an ablation experiment.

Development and evaluation of a hybrid neural network-based model structure improve the prediction of daily blood donations. This intelligent forecasting method can help managers to overcome the challenges of sudden blood demand and contribute to the optimization of resource allocation tasks.