Research on intelligent management of diagnosis and treatment equipment for chronic respiratory diseases based on mutual information particle swarm optimization-long short-term memory prediction model
10.3969/j.issn.1672-8270.2024.09.021
- VernacularTitle:基于互信息粒子群优化-长短期记忆神经网络医疗设备运行质量预测模型的慢性呼吸系统疾病诊疗设备智能管理研究
- Author:
Jia LIU
1
;
Jing LI
;
Qiuran MU
;
Zhezhi WU
Author Information
1. 新疆维吾尔自治区人民医院运营管理与事业发展部 乌鲁木齐 830001
- Keywords:
Long short-term memory network;
Particle swarm optimization algorithm;
Intelligent management;
Equipment operating quality;
Prediction model
- From:
China Medical Equipment
2024;21(9):107-112
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a prediction model for the operation quality of medical equipment based on mutual information particle swarm optimization(PSO)-long short-term memory(LSTM)neural network to assist the intelligent management of diagnosis and treatment equipment for chronic respiratory diseases.Methods:The basic data,usage data,maintenance data and performance data of equipment were collected for denoising and standardized processing,and a PSO-LSTM prediction model was constructed,and intelligent management plans for equipment use,maintenance,repair and scrapping were formulated.A total of 139 medical equipment in clinical use in the Respiratory Department of the People's Hospital of Xinjiang Uygur Autonomous Region from August 2019 to July 2023 was selected.67 devices from August 2019 to July 2021 adopted the experience management mode,and 72 devices from August 2021 to July 2023 adopted the intelligent management mode.The prediction accuracy of traditional recurrent neural network(RNN),LSTM neural network model training and test set,and PSO-LSTM neural network model were calculated.The equipment management quality of the two management modes and the satisfaction of equipment operators,technical support personnel,patients and their families with the two management modes were compared.Results:The mean absolute percentage error(MAPE)and root mean square error(RMSE)values of the prediction accuracy of the PSO-LSTM model training set are 0.014 and 0.008,respectively,and the test set is 0.032 and 0.018,respectively,both lower than RNN and RMSE.The failure rate,start-up rate,management cost increase,maintenance implementation rate and scrap compliance rate of the intelligent management mode were(0.99±0.85)times/year,(95.74±2.16)%,(1.72±1.28)%,(96.49±1.97)%and(97.59±1.49%),respectively,and the increase of fault frequency and management cost were lower than those of the experience management mode,while the start-up rate,maintenance implementation rate and scrap compliance rate were than those of the experience management mode,the difference was statistically significant(t=3.297,3.469,2.394,4.187,3.503,P<0.05).The satisfaction scores of equipment operators and technical support personnel and patients and their families on the performance,operating quality,management method,management cost and diagnosis and treatment effect of the equipment of the intelligent management mode were(94.73±1.85),(93.38±3.15),(93.48±2.02),(94.35±2.34)and(95.14±2.07),respectively,which were all higher than those of the experience management,the difference was statistically significant(t=4.131,3.827,5.716,3.430,3.173,P<0.05).Conclusion:PSO-LSTM neural network prediction model can more accurately evaluate the operating status of medical equipment,improve the clinical operation quality of medical equipment and improve clinical service satisfaction.
