Evidence map›Paper›PMID 38403618›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2024

[Research on fault diagnosis of patient monitor based on text mining].

Xiangfei He, Hehua Zhang, Jing Huang, Dechun Zhao, Yang Li, Rui Nie, Xianghua Liu

Open access · greenAbstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Xiangfei HeSchool of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China.
Hehua ZhangDepartment of Medical Engineering, Daping Hospital of Army Medical University, Chongqing 400042, P. R. China.
Jing HuangDepartment of Medical Engineering, Daping Hospital of Army Medical University, Chongqing 400042, P. R. China.
Dechun ZhaoSchool of Biological Information, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China.
Yang LiDepartment of Medical Engineering, Daping Hospital of Army Medical University, Chongqing 400042, P. R. China.
Rui NieDepartment of Medical Engineering, Daping Hospital of Army Medical University, Chongqing 400042, P. R. China.
Xianghua LiuDepartment of Medical Engineering, Daping Hospital of Army Medical University, Chongqing 400042, P. R. China.
Chongqing University of Posts and Telecommunications · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

Indexed as

Data MiningElectric Power SuppliesHumansMonitoring, PhysiologicAttention mechanismFault diagnosisPatient monitorPre-trained language modelsText mining

Identifiers

PMID38403618
PMCPMC10894744
OpenAlexW4394764062

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.