Research on Consumption Ratio Prediction Based on BP Neural Network
Objective To use the backward propagation(BP)neural network to establish an appropriate consumption ratio prediction model,to help the hospital management department evaluate whether the use of consumables in each department is reasonable.Methods The operating data of the First Affiliated Hospital of Anhui Medical University from January 2021 to May 2023 were selected to construct the dataset.The network model was trained through the training set and its performance was evaluated through the validation set and the test set.Results The BP neural network model was established to predict the consumption ratio.The explained variance was 0.998604 in the validation set,and the mean absolute error was 0.006219.The evaluation index decreased slightly in the test set,the explained variance was 0.962396,and the mean absolute error was 0.027858.All evaluation indexes were better than other models.Conclusion The consumption ratio prediction model based on BP neural network can realize the nonlinear relationship description of the department,total revenue,drug ratio,and hospital visits etc.,and the model output can accurately predict the consumption ratio,which provides quantitative data support for the assessment and evaluation of consumables in each department.
medical consumablesconsumption ratiobackward propagation neural networkregression model
万方数据
维普
