Research on fault diagnosis of patient monitor based on text mining
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:41 |
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| Enthalten in: |
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi - 41(2024), 1 vom: 25. Feb., Seite 168-176 |
| Sprache: |
Chinesisch |
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| Beteiligte Personen: |
He, Xiangfei [VerfasserIn] |
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| Links: |
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| Themen: |
Attention mechanism |
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| Anmerkungen: |
Date Completed 27.02.2024 Date Revised 28.02.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.7507/1001-5515.202306017 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368935981 |
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| 245 | 1 | 0 | |a Research on fault diagnosis of patient monitor based on text mining |
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| 500 | |a Date Revised 28.02.2024 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 650 | 4 | |a English Abstract | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Attention mechanism | |
| 650 | 4 | |a Fault diagnosis | |
| 650 | 4 | |a Patient monitor | |
| 650 | 4 | |a Pre-trained language models | |
| 650 | 4 | |a Text mining | |
| 700 | 1 | |a Zhang, Hehua |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Dechun |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Nie, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Xianghua |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi |d 1997 |g 41(2024), 1 vom: 25. Feb., Seite 168-176 |w (DE-627)NLM093699425 |x 1001-5515 |7 nnns |
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