Research on risk management model of ECG monitoring equipment in emergency department based on particle swarm optimization algorithm
10.3969/j.issn.1672-8270.2024.06.028
- VernacularTitle:基于粒子群优化算法的急诊科心电监护设备风险管理模式研究
- Author:
Baiming ZHENG
1
;
Xiaoqi SUN
;
Zheng CHEN
;
Jia WANG
Author Information
1. 北京市普仁医院医院办公室 北京 100062
- Keywords:
Neural network model;
Particle swarm optimization(PSO)algorithm;
ECG monitoring equipment;
Risk management
- From:
China Medical Equipment
2024;21(6):143-148
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct equipment risk management model based on particle swarm optimization(PSO)algorithm,and to discuss its application value in the management of ECG monitoring equipment in emergency department.Methods:The operation risk data of ECG monitoring equipment in the hospital were collected and normalized,and the PSO algorithm was used to optimize the neural network model to construct the risk management model of ECG monitoring equipment.30 ECG monitoring equipment in clinical use in the emergency department of Beijing Puren Hospital from November 2021 to October 2023 were selected and according to different equipment management modes,the backpropagation(BP)neural network model(referred to as the conventional BP model)and the PSO algorithm equipment risk management model(referred to as the PSO algorithm model)were used to manage the equipment respectively.The equipment risk fault identification effect,alarm risk control effect and equipment fault maintenance diagnosis time were compared between two management models.Results:The area under the curve(AUC)of the receiver operating characteristic(ROC)value,accuracy,sensitivity,and specificity of risk fault data identification in the test set using the PSO algorithm were 0.869,93.6%,92.8 and 95.1%,respectively,the AUC value,accuracy,sensitivity,and specificity of risk fault data identification in the training set were 0.839,95.6%,97.9%and 96.7%,respectively,which were higher than those of the conventional BP model,and the difference was statistically significant(x2test=3.691,4.023,3.557,3.409,x2training=6.884,5.962,5.334,3.215,P<0.05).The pass rate of ECG monitoring equipment alarm threshold and the average pass rate of equipment maintenance using PSO algorithm were(98.61±3.07)%and(98.79±3.11)%,respectively,which were higher than those of the conventional BP mode,and the alarm mute rate was(1.14±0.27)%,which was lower than that of the conventional BP mode,and the differences were statistically significant(Z=11.831,10.020,21.141,P<0.05).The internal repair time,external repair time,fault diagnosis time and total repair time of ECG monitoring equipment using PSO algorithm were(1.21±0.96)min,(3.18±1.09)min,(5.08±1.93)min and(10.95±2.81)min,respectively,which were all less than those of the conventional BP mode,the difference was statistically significant(t=15.404,19.020,16.694,25.511,P<0.05).Conclusion:The application of the risk management model of ECG monitoring equipment based on PSO algorithm can improve the sensitivity,specificity and accuracy of risk fault data identification of ECG monitoring equipment,improve the qualified rate of alarm threshold and equipment maintenance,reduce the silence rate of alarm,and shorten the time of fault diagnosis and repair.
