Abstract：Objective To propose a BP neural network model optimized by particle swarm optimization, to establish a potential
mapping relationship between ventilator monitoring data and ventilator failure, thereby providing a reference for ventilator repair
and preventive maintenance. Methods This paper introduced the establishment process of BP neural network model, particle swarm
optimization algorithm and particle swarm optimization combined with BP neural network model. The operation data of a signal
ventilator collected in our hospital from January 1, 2017 to December 31, 2020 was selected as the research object. We randomly
divided the fault data set into training set (n=246) and test set (n=164) according to the ratio of 6∶4. The BP neural network model
and the BP neural network model optimized by particle swarm optimization algorithm were respectively trained and tested using the
training set and test set. The accuracy, AUC value, sensitivity, and specificity were used as model evaluation indicators. Results The
accuracy, AUC value, sensitivity and specificity of BP neural network model optimized by particle swarm optimization were 0.921,
0.811, 0.923 and 0.942, respectively. Compared with K-NN, NBC, SVM and BP models, the accuracy of PSO-BP neural network
model increased by 10.4%, 11.0%, 5.2% and 9.7%, respectively, and the effect was significantly improved (P<0.05). The AUC value,
sensitivity and specificity were improved to some extent. Conclusion The BP neural network model optimized by swarm algorithm
proposed in this paper has a good effect on fault prediction, and can provide new ideas for ventilator fault diagnosis and preventive
maintenance.
罗旭. PSO优化BP神经网络模型在医疗设备故障识别中的应用[J]. 中国医疗设备, 2022, 37(12): 49-52.
LUO Xu. Application of PSO Optimized BP Neural Network Model in
Medical Equipment Fault Identification. China Medical Devices, 2022, 37(12): 49-52.
