Research on Prediction of Blood Transfusion Based on GPBO-LST-Attention

Blood is vital to sustain human life, to ensure that hospitals or blood centers have sufficient blood reserves, this paper is based on Long- and Short-Term Temporal Patterns with Deep Neural Networks (LSTNET) and Attention Mechanism, uses Gaussian Process Bayesian Optimization (GPBO) to optimize the model, and propose GPBO-LST-Attention network model. Based on the clinical blood transfusion data of a medical institution in Harbin in the past 10 years, the clinical blood transfusion volume was modeled and the blood usage in the future was predicted. The model proposed in this paper was subjected to ablation experiments and compared with other models, using the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) analyses to evaluate the model performance, and Akaike Information Criterion corrected (AICc) was used to analyze the model performance. It is shown that when the attention module and the GPBO algorithm are used in the GPBO-LST-Attention model proposed in this paper, the RMSE is reduced by 0.789, the MAE by 0.592, and the MAPE by 0.49. The prediction error values of error indexes and the AICc value are lower than the other models. It shows that the model established in this paper can accurately predict blood consumption in a future time and has excellent model performance, which can provide data reference for the blood reserve of hospitals or blood centers.