An applied mathematical model based on discrete regression algorithm for computational prediction of blood collection volume

To make blood collection more accurate, we propose a dynamic model of blood collection based on a discrete regression algorithm. This paper proposes a dynamic model of blood collection based on a discrete regression algorithm mathematical model. By defining the data parameters of the collection model and using the blood collection example data as a reference, we analyze the factors affecting the blood collection process, including blood inventory control, blood demand fluctuation, safety stock, and target stock level, to obtain the required blood collection volume. The analysis of the case data shows that moderately increasing the target stock level and safety stock is conducive to reducing the shortage, thus understanding that the current blood collection volume is not in high demand. Improving blood demand forecasting can improve blood security and can have a significant impact on the value of blood collection. Therefore, before blood collection, a blood collection dynamic model can be used to make correct and reasonable inventory control parameters and calculate a clear blood collection volume.