Objective:To conduct an in-depth analysis of endoscope maintenance data using text mining techniques,establish a maintenance level prediction model,and identify key features to optimize maintenance management decisions.Methods:A total of 19 676 maintenance data of Olympus endoscope from 2005 to 2020 in Southwest China were collected.The Jieba segmentation-TF-IDF combined feature extraction was adopted to process fault text.A classification model about maintenance grade was constructed on the basis of Xgboost algorithm.And then,the feature selection algorithm was combined to identify key feature.Results:The model achieved an accuracy of 90%,AUC of 0.85,sensitivity of 76%,specificity of 95%,and F1-score of 0.80.The top 10 features ranked by importance were:image abnormality,wear,leakage,insertion tube,device category,light guide tube,button,angle malfunction,CCD glass,and rubber.Conclusion:The text mining-based prediction model for maintenance grade of endoscope can accurately predict the grade of repair.The paper provides suggestions for engineers to pay key attention to preventive maintenance for insertion tube,CCD glass of endoscope,so as to reduce cost and enhance usage efficiency of equipment.

Objective:To conduct an in-depth analysis of endoscope maintenance data using text mining techniques,establish a maintenance level prediction model,and identify key features to optimize maintenance management decisions.Methods:A total of 19 676 maintenance data of Olympus endoscope from 2005 to 2020 in Southwest China were collected.The Jieba segmentation-TF-IDF combined feature extraction was adopted to process fault text.A classification model about maintenance grade was constructed on the basis of Xgboost algorithm.And then,the feature selection algorithm was combined to identify key feature.Results:The model achieved an accuracy of 90%,AUC of 0.85,sensitivity of 76%,specificity of 95%,and F1-score of 0.80.The top 10 features ranked by importance were:image abnormality,wear,leakage,insertion tube,device category,light guide tube,button,angle malfunction,CCD glass,and rubber.Conclusion:The text mining-based prediction model for maintenance grade of endoscope can accurately predict the grade of repair.The paper provides suggestions for engineers to pay key attention to preventive maintenance for insertion tube,CCD glass of endoscope,so as to reduce cost and enhance usage efficiency of equipment.

In order to collect operation data and fault data of medical equipment,a Wi-Fi-based Internet of Things(IoT)acquisition device for medical equipment was designed.The design of the acquisition device based on the integration of STM32F407 32-bit microcontroller and ESP8266 wireless module,and the IoTs data acquisition device conducted communication through Wi-Fi.Through the acquisition device,the operation data of the medical equipment could be read through the data interface such as serial port after the baud rate was configured.The data interaction with the servers of IoT platform could be realized through the connection between distribution network of wireless modules and access point(AP)of wireless at the network of each floor,which could reach dynamic management for medical equipment.The application of data acquisition of a series of equipment,which included ventilator,high-frequency electrotome,monitor,hemodialysis machine and other equipment,could be realized in the IoT platform software through assembled and apply this acquisition device,and it can solve the problem of data communication of medical equipment that cannot realize wireless communication.Under the premise of obtaining communication protocols,and it can adapt to more data acquisition and data transmission functions of this type of medical equipment,and realize dynamic management of hospital for this type of medical equipment.