Evidence map›Paper›PMID 33401250›Full record

ArticleAging2020

Construction and analysis of a joint diagnosis model of random forest and artificial neural network for heart failure.

Yuqing Tian, Jiefu Yang, Ming Lan, Tong Zou

Open access · greenAbstract read
In one paragraph

Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
2.9field-weighted citation impact, top 7% 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

41 citing papers in PubMed, 64 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Construction of a diagnostic model based on random forest and artificial neural network for peri-implantitis.Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2024
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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

4 authors at 2 institutions in 1 country.

Yuqing TianPeking University Fifth School of Clinical Medicine, Beijing 100730, P.R. China.
Jiefu YangDepartment of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Science, Beijing 100730, P.R. China.
Ming LanDepartment of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Science, Beijing 100730, P.R. China.
Tong ZouPeking University Fifth School of Clinical Medicine, Beijing 100730, P.R. China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNPeking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heart failure is a global health problem that affects approximately 26 million people worldwide. As conventional diagnostic techniques for heart failure have been in practice with various limitations, it is necessary to develop novel diagnostic models to supplement existing methods. With advances and improvements in gene sequencing technology in recent years, more heart failure-related genes have been identified. Using existing gene expression data in the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) of heart failure and identified six key genes (

Indexed as

Neural Networks, ComputerTranscriptomeDatabases, GeneticHeart FailureHeme Oxygenase (Decyclizing)HumansLipocalinsMachine LearningReceptors, InterleukinRNA-Binding ProteinsRNA Splicing FactorsSerpinsCSDC2 protein, humanFrem1 protein, humanheme oxygenase-2Heme Oxygenase (Decyclizing)LCN6 protein, humanLipocalinsReceptors, InterleukinRNA-Binding ProteinsRNA Splicing FactorsSERPINA3 protein, humanSerpinsartificial neural networkdifference analysisheart failurerandom forest

Identifiers

PMID33401250
PMCPMC7803554
OpenAlexW3116283216

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.