Construction and application of prediction model based on text mining for maintenance grade of endoscope
10.3969/j.issn.1672-8270.2025.09.030
- VernacularTitle:基于文本挖掘的内窥镜维修等级预测模型构建及应用
- Author:
Yixuan ZHUO
1
;
Jiang DU
;
Luokuan YANG
;
Pengxin YE
;
Qilin LIU
Author Information
1. 四川大学华西医院医学工程科 成都 610041
- Publication Type:Journal Article
- Keywords:
Electronic endoscope;
Text mining;
Machine learning;
Selection of feature;
Prediction model
- From:
China Medical Equipment
2025;22(9):155-157,162
- CountryChina
- Language:Chinese
-
Abstract:
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.
