Evidence map›Paper›PMID 38188510›Full record

ArticleiScience2024

Employing broad learning and non-invasive risk factor to improve the early diagnosis of metabolic syndrome.

Junwei Duan, Yuxuan Wang, Long Chen, C L Philip Chen, Ronghua Zhang

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

Article in iScience, 2024. 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
0.4field-weighted citation impact, top 39% 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, 2 citations in OpenAlex.

  1. Review
  2. Reinforced Collaborative-Competitive Representation for Biomedical Image Recognition.Interdisciplinary sciences, computational life sciences · 2025
    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

5 authors at 3 institutions in 2 countries.

Junwei DuanCollege of Information Science and Technology, Jinan University, Guangzhou, Guangdong 511436, China.
Yuxuan WangJinan University - University of Birmingham Joint Institute, Jinan University, Guangzhou, Guangdong 511436, China.
Long ChenDepartment of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau 999078, China.
C L Philip ChenSchool of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.
Ronghua ZhangCollege of Pharmacy, Jinan University, Guangzhou, Guangdong 510006, China.
Jinan University · CNSouth China University of Technology · CNUniversity of Macau · MO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic syndrome (MetS) as a multifactorial disease is highly prevalent in countries and individuals. Monitoring the conventional risk factors (CRFs) would be a cost-effective strategy to target the increasing prevalence of MetS and the potential of noninvasive CRF for precisely detection of MetS in the early stage remains to be explored. From large-scale multicenter MetS clinical dataset, we discover 15 non-invasive CRFs which have strong relevance with MetS and first propose a broad learning-based approach named Genetic Programming Collaborative-competitive Broad Learning System (GP-CCBLS) with noninvasive CRF for early detection of MetS. The proposed GP-CCBLS model can significantly boost the detection performance and achieve the accuracy of 80.54%. This study supports the potential clinical validity of noninvasive CRF to complement general diagnostic criteria for early detecting the MetS and also illustrates possible strength of broad learning in disease diagnosis comparing with other machine learning approaches.

Indexed as

Human metabolismMachine learningRisk factor

Identifiers

PMID38188510
PMCPMC10770709
OpenAlexW4389346840

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.