Research on fault diagnosis of patient monitor based on text mining_Journal of Biomedical Engineering

Authors：

HE Xiangfei ¹ ,  ZHANG Hehua ^2,3 , HUANG Jing ² , ZHAO Dechun ³ , LI Yang ² , NIE Rui ² , LIU Xianghua ²

1. School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China;
2. Department of Medical Engineering, Daping Hospital of Army Medical University, Chongqing 400042, P. R. China;
3. School of Biological Information, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China;

Corresponding author：

ZHANG Hehua, Email: zhanghehua@vip.163.com

Keywords：

Patient monitor; Fault diagnosis; Text mining; Pre-trained language models; Attention mechanism

DOI：

10.7507/1001-5515.202306017

Video：

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The conventional fault diagnosis of patient monitors heavily relies on manual experience, resulting in low diagnostic efficiency and ineffective utilization of fault maintenance text data. To address these issues, this paper proposes an intelligent fault diagnosis method for patient monitors based on multi-feature text representation, improved bidirectional gate recurrent unit (BiGRU) and attention mechanism. Firstly, the fault text data was preprocessed, and the word vectors containing multiple linguistic features was generated by linguistically-motivated bidirectional encoder representation from Transformer. Then, the bidirectional fault features were extracted and weighted by the improved BiGRU and attention mechanism respectively. Finally, the weighted loss function is used to reduce the impact of class imbalance on the model. To validate the effectiveness of the proposed method, this paper uses the patient monitor fault dataset for verification, and the macro F1 value has achieved 91.11%. The results show that the model built in this study can realize the automatic classification of fault text, and may provide assistant decision support for the intelligent fault diagnosis of the patient monitor in the future.

Citation： HE Xiangfei, ZHANG Hehua, HUANG Jing, ZHAO Dechun, LI Yang, NIE Rui, LIU Xianghua. Research on fault diagnosis of patient monitor based on text mining. Journal of Biomedical Engineering, 2024, 41(1): 168-176. doi: 10.7507/1001-5515.202306017 Copy

1.	范莉萍, 郎朗, 肖晶晶, 等. 基于故障树的多参数监护仪故障智能诊断专家系统研究. 生物医学工程学杂志, 2022, 39(3): 586-595.
2.	Chen Mengqi, Qu Rong, Fang Weiguo. Case-based reasoning system for fault diagnosis of aero-engines. Expert Systems with Applications, 2022, 202: 117350.
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4.	Wang Z, Wang J, Chen S. Fault location of strip steel surface quality defects on hot-rolling production line based on information fusion of historical cases and process data. IEEE Access, 2020, 8: 171240-171251.
5.	侯通, 郑启明, 姚新文, 等. 基于文本挖掘的轨道电路细粒度故障致因分析方法. 铁道学报, 2022, 44(10): 73-81.
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9.	Xu Z, Chen B, Zhou S, et al. A text-driven aircraft fault diagnosis model based on a Word2vec and priori-knowledge convolutional neural network. Aerospace, 2021, 8(4): 1-15.
10.	林海香, 赵正祥, 陆人杰, 等. 基于字词融合的高铁道岔多级故障诊断组合模型. 电子测量与仪器学报, 2022, 36(10): 217-226.
11.	Huan H, Yan J, Xie Y, et al. Feature-enhanced nonequilibrium bidirectional long short-term memory model for Chinese text classification. IEEE Access, 2020, 8: 199629-199637.
12.	贾宝惠, 姜番, 王玉鑫, 等. 基于民机维修文本数据的故障诊断方法. 航空学报, 2023, 44(5): 258-272.
13.	郭崇慧, 徐良辰, 魏伟, 等. 基于文本挖掘的新型冠状病毒肺炎诊疗方案演化分析. 生物医学工程学杂志, 2021, 38(2): 197-209.
14.	Qiu X, Sun T, Xu Y, et al. Pre-trained models for natural language processing: a survey. Science China Technological Sciences, 2020, 63: 1872-1897.
15.	Jawahar G, Sagot B, Seddah D. What does BERT learn about the structure of language? // Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence: Association for Computational Linguistics, 2020: 3651–3657.
16.	Akhter M P, Zheng Jiangbin, Naqvi I R, et al. Document-level text classification using single-layer multisize filters convolutional neural network. IEEE Access, 2020, 8: 42689-42707.
17.	蒲晓蓉, 陈柯成, 刘军池, 等. COVID-19 中国诊疗方案的机器学习智能分析方法. 生物医学工程学杂志, 2020, 37(3): 365-372.
18.	沙尔旦尔·帕尔哈提, 米吉提·阿不里米提, 艾斯卡尔·艾木都拉. 基于稳健词素序列和LSTM的维吾尔语短文本分类. 中文信息学报, 2020, 34(1): 63-70.
19.	赵宏, 傅兆阳, 王乐. 基于特征融合的中文文本情感分析方法. 兰州理工大学学报, 2022, 48(3): 94-102.
20.	方正云, 杨政, 李丽敏, 等. 基于交叉注意力机制的多视图项目文本分类方法. 中文信息学报, 2022, 36(7): 123-131.
21.	刘鹏程, 孙林夫, 张常有, 等. 基于交互注意力机制网络模型的故障文本分类. 计算机集成制造系统, 2021, 27(1): 72-89.
22.	Li Y, Zhang S, Lai C. Agricultural text classification method based on dynamic fusion of multiple features. IEEE Access, 2023, 11: 27034-27042.
23.	Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate.(2016-05-19)[2023-5-23]. arXiv:1409.0473.
24.	陈可嘉, 刘惠. 基于改进BiGRU-CNN的中文文本分类方法. 计算机工程, 2022, 48(5): 59-66,73.
25.	吴庭伟, 王梦灵, 易树平, 等. 多尺度核电质量文本故障信息语义抽取方法. 中国机械工程, 2023, 34(8): 976-981,992.
26.	Xu L, Hu H, Zhang X W, et al. CLUE: a Chinese language understanding evaluation benchmark//Proceedings of the 28th International Conference on Computational Linguistics. Barcelona: International Committee on Computational Linguistics, 2020: 4762–4772.
27.	Cui Yiming, Che Wanxiang, Wang Shijin, et al. LERT: a linguistically-motivated pre-trained language model.(2022-11-10)[2023-5-23]. arXiv:2211.05344.
28.	Shi L, Li A, Zhang L. Sustainable fault diagnosis of imbalanced text mining for CTCS-3 data preprocessing. Sustainability, 2021, 13(4): 1-14.
29.	周庆华, 李晓丽. 基于MCNN的铁路信号设备故障短文本分类方法研究. 铁道科学与工程学报, 2019, 16(11): 2859-2865.
30.	邓维斌, 朱坤, 李云波, 等. FMNN: 融合多神经网络的文本分类模型. 计算机科学, 2022, 49(3): 281-287.

1. 范莉萍, 郎朗, 肖晶晶, 等. 基于故障树的多参数监护仪故障智能诊断专家系统研究. 生物医学工程学杂志, 2022, 39(3): 586-595.
2. Chen Mengqi, Qu Rong, Fang Weiguo. Case-based reasoning system for fault diagnosis of aero-engines. Expert Systems with Applications, 2022, 202: 117350.
3. 赵京胜, 宋梦雪, 高祥, 等. 自然语言处理中的文本表示研究. 软件学报, 2022, 33(1): 102-128.
4. Wang Z, Wang J, Chen S. Fault location of strip steel surface quality defects on hot-rolling production line based on information fusion of historical cases and process data. IEEE Access, 2020, 8: 171240-171251.
5. 侯通, 郑启明, 姚新文, 等. 基于文本挖掘的轨道电路细粒度故障致因分析方法. 铁道学报, 2022, 44(10): 73-81.
6. Yu D, Xiang B. Discovering topics and trends in the field of artificial intelligence: using LDA topic modeling. Expert Systems with Applications, 2023, 225: 120114.
7. Gupta R K, Agarwalla R, Naik B H, et al. Prediction of research trends using LDA based topic modeling. Global Transitions Proceedings, 2022, 3(1): 298-304.
8. Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space.(2013-09-07)[2023-5-23]. https: //arxiv.org/abs/1301.3781.
9. Xu Z, Chen B, Zhou S, et al. A text-driven aircraft fault diagnosis model based on a Word2vec and priori-knowledge convolutional neural network. Aerospace, 2021, 8(4): 1-15.
10. 林海香, 赵正祥, 陆人杰, 等. 基于字词融合的高铁道岔多级故障诊断组合模型. 电子测量与仪器学报, 2022, 36(10): 217-226.
11. Huan H, Yan J, Xie Y, et al. Feature-enhanced nonequilibrium bidirectional long short-term memory model for Chinese text classification. IEEE Access, 2020, 8: 199629-199637.
12. 贾宝惠, 姜番, 王玉鑫, 等. 基于民机维修文本数据的故障诊断方法. 航空学报, 2023, 44(5): 258-272.
13. 郭崇慧, 徐良辰, 魏伟, 等. 基于文本挖掘的新型冠状病毒肺炎诊疗方案演化分析. 生物医学工程学杂志, 2021, 38(2): 197-209.
14. Qiu X, Sun T, Xu Y, et al. Pre-trained models for natural language processing: a survey. Science China Technological Sciences, 2020, 63: 1872-1897.
15. Jawahar G, Sagot B, Seddah D. What does BERT learn about the structure of language? // Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence: Association for Computational Linguistics, 2020: 3651–3657.
16. Akhter M P, Zheng Jiangbin, Naqvi I R, et al. Document-level text classification using single-layer multisize filters convolutional neural network. IEEE Access, 2020, 8: 42689-42707.
17. 蒲晓蓉, 陈柯成, 刘军池, 等. COVID-19 中国诊疗方案的机器学习智能分析方法. 生物医学工程学杂志, 2020, 37(3): 365-372.
18. 沙尔旦尔·帕尔哈提, 米吉提·阿不里米提, 艾斯卡尔·艾木都拉. 基于稳健词素序列和LSTM的维吾尔语短文本分类. 中文信息学报, 2020, 34(1): 63-70.
19. 赵宏, 傅兆阳, 王乐. 基于特征融合的中文文本情感分析方法. 兰州理工大学学报, 2022, 48(3): 94-102.
20. 方正云, 杨政, 李丽敏, 等. 基于交叉注意力机制的多视图项目文本分类方法. 中文信息学报, 2022, 36(7): 123-131.
21. 刘鹏程, 孙林夫, 张常有, 等. 基于交互注意力机制网络模型的故障文本分类. 计算机集成制造系统, 2021, 27(1): 72-89.
22. Li Y, Zhang S, Lai C. Agricultural text classification method based on dynamic fusion of multiple features. IEEE Access, 2023, 11: 27034-27042.
23. Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate.(2016-05-19)[2023-5-23]. arXiv:1409.0473.
24. 陈可嘉, 刘惠. 基于改进BiGRU-CNN的中文文本分类方法. 计算机工程, 2022, 48(5): 59-66,73.
25. 吴庭伟, 王梦灵, 易树平, 等. 多尺度核电质量文本故障信息语义抽取方法. 中国机械工程, 2023, 34(8): 976-981,992.
26. Xu L, Hu H, Zhang X W, et al. CLUE: a Chinese language understanding evaluation benchmark//Proceedings of the 28th International Conference on Computational Linguistics. Barcelona: International Committee on Computational Linguistics, 2020: 4762–4772.
27. Cui Yiming, Che Wanxiang, Wang Shijin, et al. LERT: a linguistically-motivated pre-trained language model.(2022-11-10)[2023-5-23]. arXiv:2211.05344.
28. Shi L, Li A, Zhang L. Sustainable fault diagnosis of imbalanced text mining for CTCS-3 data preprocessing. Sustainability, 2021, 13(4): 1-14.
29. 周庆华, 李晓丽. 基于MCNN的铁路信号设备故障短文本分类方法研究. 铁道科学与工程学报, 2019, 16(11): 2859-2865.
30. 邓维斌, 朱坤, 李云波, 等. FMNN: 融合多神经网络的文本分类模型. 计算机科学, 2022, 49(3): 281-287.

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Research on fault diagnosis of patient monitor based on text mining

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