Research on Prediction Model of Invasive Ventilator Usage Based on Particle Swarm Optimization-Long and Short Memory Network Algorithm
Objective To study the prediction model of invasive ventilator usage based on particle swarm optimization long and short memory network algorithm.Methods Data on the use of invasive ventilators in the whole hospital were selected from April 2019 to April 2023.A predictive model of invasive ventilator usage based on particle swarm optimization and long and short memory network(PSO-LSTM)algorithm was established to predict the daily use of invasive ventilator in the hospital and intensive care unit.The mean absolute error(MAE),mean absolute percentage error(MAPE)and root mean square error(RMSE)were used for the evaluation accuracy indexes.Results PSO-LSTM model predicted that the MAE value,MAPE value and RMSE value of invasive ventilator in intensive ICU were reduced by 41.15%,50%and 44.36%compared with LSTM network,and PSO-LSTM model predicted that the daily consumption of invasive ventilators in the hospital was reduced by 81.93%,83.33%,and 79.08%compared with LSTM network.The PSO-LSTM of the proposed method is significantly higher than that of the LSTM network.Conclusion The PSO-LSTM model can accurately predict the daily consumption of invasive ventilators,provide a scientific basis for the procurement decision of invasive ventilators,provide a data analysis basis for the establishment of a ventilator management sharing center in the whole hospital,and improve the refined management of medical equipment.
Particle Swarm OptimizationLong and short memory network algorithmPrediction modelInvasive ventilatorUsage amount
万方数据
维普
