北京生物医学工程2025，Vol.44Issue(1)：68-73,6.DOI:10.3969/j.issn.1002-3208.2025.01.010
基于Stacking集成学习的医用直线加速器主要故障联锁预测模型研究
Research on prediction model for major fault interlock of medical linear accelerators based on stacking ensemble learning
摘要
Abstract
Objective To study the feasibility of applying the stacking ensemble learning model to the prediction of major fault interlocking of medical linear accelerators.Methods The four fault interlocks(codes:MLC,HWFA,GWIL and UDRS)with the highest frequency of Varian 23 EX linear accelerator at 119 months were retrospectively collected,and the accelerator use time(months),monthly number of treatment,monthly number of shooting fields and monthly MU were considered as the influencing factors of fault interlocking.The Stacking ensemble learning method is used to construct the prediction model of the main fault interlocking of medical linear accelerators,and the prediction accuracy and prediction performance of each base model and the ensemble learning model are evaluated by comparing the similarity,root mean square error,mean absolute value error and coefficient of determination between the fault interlocking frequency curve and the real fault interlocking frequency curve.Results Compared with the base models,the fault interlocking frequency curves of the ensemble learning model are more similar to the real fault interlocking frequency curves,and the root mean square error,mean absolute value error and coefficient of determination of the ensemble learning model are 0.41,0.33 and 83.2％in MLC interlock fault prediction,respectively.In the prediction of HWFA interlock faults,they were 0.19,0.17 and 74.2％,respectively.In the GFIL interlock fault prediction,they were 0.19,0.16 and 67.9％,respectively.In the UDRS interlock fault prediction,they are 0.20,0.17 and 71.5％,respectively.The results of each indicator were better than the single base model.Conclusions Based on the Stacking ensemble learning model,the main fault interlocking trend of linear accelerator can be predicted more accurately,which has certain application value for preventive maintenance and fault repair management of accelerator.关键词
直线加速器/故障联锁预测/集成学习/长短期记忆网络Key words
Linear accelerator/Fault interlocking prediction/Ensemble learning/Long short-term memory network分类
医药卫生引用本文复制引用
李亮,何威震,沙冠辰,解昕,陈勇,章龙珍..基于Stacking集成学习的医用直线加速器主要故障联锁预测模型研究[J].北京生物医学工程,2025,44(1):68-73,6.基金项目
国家自然科学基金面上项目(81972845)、徐州市引进临床医学专家团队项目(2019TD003)资助 （81972845）
