Evidence map›Paper›PMID 34367315›Full record

ArticleComputational and mathematical methods in medicine2021

A Self-Representation-Based Fuzzy SVM Model for Predicting Vascular Calcification of Hemodialysis Patients.

Xiaobin Liu, Xiran Zhang, Xiaoyi Guo, Yijie Ding, Weiwei Shan, Liang Wang, Wei Zhou, Hua Shi

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.1field-weighted citation impact, top 16% of its field
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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. KernelizedFrontiers in neuroscience · 2021
    Article
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

8 authors at 3 institutions in 1 country.

Xiaobin LiuDepartment of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214023, Wuxi, China.
Xiran ZhangDepartment of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214023, Wuxi, China.
Xiaoyi GuoDepartment of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214023, Wuxi, China.
Yijie DingSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, 215009, Suzhou, China.ORCID https://orcid.org/0000-0003-2911-7643
Weiwei ShanDepartment of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214023, Wuxi, China.
Liang WangDepartment of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214023, Wuxi, China.ORCID https://orcid.org/0000-0002-1456-4718
Wei ZhouDepartment of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214023, Wuxi, China.ORCID https://orcid.org/0000-0002-0507-2033
Hua ShiSchool of Opto-Electronic and Communication Engineering, Xiamen University of Technology, 365001, Xiamen, China.ORCID https://orcid.org/0000-0001-8812-5737
Wuxi People's Hospital · CNSuzhou University of Science and Technology · CNXiamen University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In end-stage renal disease (ESRD), vascular calcification risk factors are essential for the survival of hemodialysis patients. To effectively assess the level of vascular calcification, the machine learning algorithm can be used to predict the vascular calcification risk in ESRD patients. As the amount of collected data is unbalanced under different risk levels, it has an influence on the classification task. So, an effective fuzzy support vector machine based on self-representation (FSVM-SR) is proposed to predict vascular calcification risk in this work. In addition, our method is also compared with other conventional machine learning methods, and the results show that our method can better complete the classification task of the vascular calcification risk.

Indexed as

Support Vector MachineAdultAgedAlgorithmsAorta, AbdominalComputational BiologyFemaleFuzzy LogicHeart Disease Risk FactorsHumansKidney Failure, ChronicLogistic ModelsMachine LearningMaleMiddle AgedNeural Networks, Computer

Identifiers

PMID34367315
PMCPMC8337133
OpenAlexW3184317053

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.