Outpatient Volume Prediction Model Based on CEEMDAN-VMD-SSA-LSTM
Hospital outpatient volume is essentially a time series with potential laws.Through effective analysis and prediction of outpatient volume,medical resources can be more scientifically and reasonably allocated.A CEEMDAN-VMD-SSA-LSTM model is proposed to predict the time series with large fluctuation of outpatient volume.The data are decomposed twice by fully adaptive noise complete set empirical mode decomposition(CEEMDAN)and variational mode decomposition(VMD),which improves the accuracy and stability of the outpatient data set.The long short-term memory(LSTM)neural network with good performance in time series problem processing is adopted,and the super parameters of LSTM network are optimized by spar-row search algorithm(SSA)with strong optimization ability and good stability.Through comparative experiments,the pro-posed method can more accurately predict and analyze the outpatient volume,providing an important basis and decision support for better operation and management of the hospital.
万方数据
维普
