[1]刘倩,张梅,马铃,等.改进型深度学习网络分析婴儿培养箱质控数据变化趋势的预防性维护[J].中国医学物理学杂志,2026,43(7):987-996.[doi:DOI:10.3969/j.issn.1005-202X.2026.07.021]
　LIU Qian? ZHANG Mei,MA Ling? XIE Yongbin? ZHAO Yuhan?/html>.Preventive maintenance of infant incubators based on quality control data trend analysis using an improved deep learning network[J].Chinese Journal of Medical Physics,2026,43(7):987-996.[doi:DOI:10.3969/j.issn.1005-202X.2026.07.021]
点击复制

改进型深度学习网络分析婴儿培养箱质控数据变化趋势的预防性维护()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
43卷
期数:
2026年第7期
页码:
987-996
栏目:
医学人工智能
出版日期:
2026-07-22

文章信息/Info

Title:
Preventive maintenance of infant incubators based on quality control data trend analysis using an improved deep learning network
文章编号:
1005-202X（2026）07-0987-10
作者:
刘倩1; 张梅2; 马铃1; 谢永斌1; 赵予涵3
1.北京大学第一医院宁夏妇女儿童医院医学装备办公室， 宁夏 银川750021； 2.北京大学第一医院宁夏妇女儿童医院急诊科， 宁夏 银川 750021； 3.北京大学第一医院医学装备处， 北京 100034
Author(s):
LIU Qian? ZHANG Mei2;  MA Ling? XIE Yongbin? ZHAO Yuhan?/html>
1. Medical Equipment Office, Peking University First Hospital-Ningxia Women and Childrens Hospital, Yinchuan 750021, China 2. Emergency Department, Peking University First Hospital-Ningxia Women and Childrens Hospital, Yinchuan 750021, China 3. Medical Equipment Department, Peking University First Hospital, Beijing 100034, China
关键词:
婴儿培养箱; 深度学习; 质控数据; 趋势预测; 预防性维护
Keywords:
Keywords: infant incubator deep learning quality control data trend prediction preventive maintenance
分类号:
R318；R197.39
DOI:
DOI:10.3969/j.issn.1005-202X.2026.07.021
文献标志码:
A
摘要:
目的：开发一种改进型深度学习网络，通过分析婴儿培养箱质控数据的微小变化趋势，将传统“阈值判定型”的被动维护模式转变为“趋势预测型”的主动干预机制，从而改进高风险医疗设备的预防性维护策略。方法：以婴儿培养箱相邻两次质控检测参数的差值构建特征矩阵，将数据划分为正常运行与故障预警类别。构建改进型TabNet深度学习网络，通过差异敏感特征注意力机制和多尺度特征融合模块增强对参数微小变化的识别能力。多维度评估模型的分类性能、预测准确性、风险量化准确度及设备维护决策价值，并与原始TabNet、深度神经网络及传统机器学习算法进行对比验证。通过特征重要性分析识别影响故障预测的关键参数，揭示婴儿培养箱性能退化的内在机理。结果：改进型TabNet模型在独立测试集上达到92.3%的准确率和75.0%的召回率，显著优于原始TabNet模型87.5%的准确率和7.1%的召回率，同时超越深度神经网络及传统机器学习方法。决策曲线分析进一步证实其在广泛风险阈值范围内均提供最大净获益。特征重要性分析显示，升温时间和相对湿度是预测模型中最具贡献的指标，其累积贡献率接近40%，而操作门附近D区的温度参数异常也显示出重要的预警价值，为设备早期性能退化的识别提供关键信息。结论：本研究成功构建基于改进型深度学习网络的婴儿培养箱预防性维护预测模型，实现对设备性能渐变式衰减的早期识别，推动医疗设备管理从被动响应向主动干预转变，对保障早产儿治疗环境安全具有重要的价值。
Abstract:
Abstract: Objective To develop an improved deep learning network to convert traditional "threshold-based" passive maintenance into "trend prediction-based" proactive intervention through the analysis of subtle changes in infant incubator quality control data, thereby optimizing preventive maintenance strategies for high-risk medical equipment. Methods Feature matrices were constructed from differences between adjacent quality control parameters of infant incubators, with data categorized into normal-condition and fault-warning classes. An improved TabNet deep learning network was developed to improve the detection of subtle parameter changes by incorporating differential-sensitive feature attention mechanisms and multi-scale feature fusion modules. Model performance was comprehensively evaluated across multiple dimensions, including classification capability, prediction accuracy, risk quantification, and decision-making value for equipment maintenance. The proposed model was then compared with original TabNet, deep neural networks, and traditional machine learning algorithms for validation. Feature importance analysis was used to identify key parameters affecting fault prediction and reveal the mechanisms underlying incubator performance degradation. Results The improved TabNet model achieved 92.3% accuracy and 75.0% recall rate on the independent test set, significantly higher than 87.5% accuracy and 7.1% recall rate of the original TabNet, while also outperforming deep neural networks and traditional machine learning methods. Decision curve analysis further confirmed its maximum net benefit across a wide range of risk thresholds. Feature importance analysis identified heating time and relative humidity as the most contributive indicators, with a cumulative contribution rate approaching 40%. Temperature abnormalities near the operation door (zone D) also exhibited significant warning value, providing crucial information for the early detection of equipment performance degradation. Conclusion A preventive maintenance prediction model for infant incubators is successfully constructed based on an improved deep learning network, enabling early identification of progressive equipment performance degradation, and transforming medical equipment management from passive troubleshooting to proactive intervention, with significant value for safeguarding the safety of premature infants treatment environment.

相似文献/References:

[1]陶源,王佳飞,杜俊龙,等.基于卷积神经网络的细胞识别[J].中国医学物理学杂志,2017,34(1):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
　[J].Chinese Journal of Medical Physics,2017,34(7):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
[2]门阔,戴建荣. 利用深度反卷积神经网络自动勾画放疗危及器官[J].中国医学物理学杂志,2018,35(3):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
　MEN Kuo,DAI Jianrong. Automatic segmentation of organs at risk in radiotherapy using deep deconvolutional neural network[J].Chinese Journal of Medical Physics,2018,35(7):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
[3]邓金城,彭应林,刘常春,等. 深度卷积神经网络在放射治疗计划图像分割中的应用[J].中国医学物理学杂志,2018,35(6):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
　DENG Jincheng,PENG Yinglin,LIU Changchun,et al. Application of deep convolution neural network in radiotherapy planning image segmentation[J].Chinese Journal of Medical Physics,2018,35(7):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
[4]查雪帆,杨丰,吴俣南,等. 结合迁移学习与深度卷积网络的心电分类研究[J].中国医学物理学杂志,2018,35(11):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
　ZHA Xuefan,YANG Feng,WU Yunan,et al. ECG classification based on transfer learning and deep convolution neural network[J].Chinese Journal of Medical Physics,2018,35(7):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
[5]宫进昌,赵尚义,王远军. 基于深度学习的医学图像分割研究进展[J].中国医学物理学杂志,2019,36(4):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
　GONG Jinchang,ZHAO Shangyi,WANG Yuanjun.Research progress on deep learning-based medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(7):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
[6]安莹,黄能军,杨荣,等. 基于深度学习的心血管疾病风险预测模型[J].中国医学物理学杂志,2019,36(9):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
　AN Ying,HUANG Nengjun,YANG Rong,et al. Deep learning-based model for risk prediction of cardiovascular diseases[J].Chinese Journal of Medical Physics,2019,36(7):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
[7]徐航,随力,张靖雯,等.卷积神经网络在医学图像分割中的研究进展[J].中国医学物理学杂志,2019,36(11):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
　XU Hang,SUI Li,ZHANG Jingwen,et al.Progress on convolutional neural network in medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(7):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
[8]张富利,崔德琪,王秋生,等.基于深度学习和图谱库方法自动勾画肿瘤放疗中危及器官的比较[J].中国医学物理学杂志,2019,36(12):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
　ZHANG Fuli,CUI Deqi,WANG Qiusheng,et al.Comparative study of deep learning- versus Atlas-based auto-segmentation of organs-at-risk in tumor radiotherapy[J].Chinese Journal of Medical Physics,2019,36(7):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
[9]温佳圆,林国钰,张逸文,等.应用深度学习网络实现肾小球滤过膜超微病理图像的语义分割[J].中国医学物理学杂志,2020,37(2):195.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.012]
　WEN Jiayuan,LIN Guoyu,ZHANG Yiwen,et al.Semantic segmentation of ultrastructural pathological images of glomerular filtration membrane using deep learning network[J].Chinese Journal of Medical Physics,2020,37(7):195.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.012]
[10]秦楠楠,薛旭东,吴爱林,等.基于U-net卷积神经网络的宫颈癌临床靶区和危及器官自动勾画的研究[J].中国医学物理学杂志,2020,37(4):524.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.023]
　QIN Nannan,XUE Xudong,WU Ailin,et al.Automatic segmentation of clinical target volumes and organs-at-risk in radiotherapy for cervical cancer using U-net convolutional neural network[J].Chinese Journal of Medical Physics,2020,37(7):524.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.023]

备注/Memo

备注/Memo:
【收稿日期】2026-01-04 【基金项目】宁夏回族自治区卫生健康系统科研课题（2024-NWQP-B058）；北京大学第一医院宁夏妇女儿童医院院级科研课题（YJKY-2023-05） 【作者简介】刘倩，副主任护师，研究方向：医学装备管理，E-mail: nxfyyxzb@163.com 【通信作者】赵予涵，硕士，副研究员，研究方向：医学装备管理，E-mail: 67440848@qq.com
更新日期/Last Update: 2026-07-23