Evidence mapPaperPMID 40534992Full record

ArticleMolecular therapy. Nucleic acids2025

DRPM: An advanced predictive model for early diabetes detection and risk stratification.

Fulei Nie, Xiaoming Song, Wei Chen

Abstract read
In one paragraph

Article in Molecular therapy. Nucleic acids, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Fulei NieSchool of Public Health, North China University of Science and Technology, Tangshan 063210, China.
Xiaoming SongSchool of Life Sciences, North China University of Science and Technology, Tangshan 063210, China.
Wei ChenSchool of Public Health, North China University of Science and Technology, Tangshan 063210, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetes is a prevalent chronic disease that poses a significant burden on individuals and healthcare systems. Early diagnosis and effective management are essential for delaying disease onset and minimizing complications. In this study, we developed a deep learning-based risk prediction model using data from the National Health and Nutrition Examination Survey (NHANES) spanning from 2011 to 2018. From an initial set of 36 variables, and through feature selection, five key features were identified through feature selection techniques to construct the model. The model demonstrated robust performance, accurately predicting diabetes risk with high precision in test data. External validation further confirmed its ability to correctly identify individuals at risk of developing diabetes. To enhance its practical application, we implemented a risk stratification system and developed a user-friendly online tool, available at http://cbcb.cdutcm.edu.cn/drpm/, allowing easy access for users. This model provides a valuable tool for diabetes risk screening and personalized early detection.

Indexed as

deep learningdiabetes mellitusdiabetes risk predictionmodel interpretabilityMT: Bioinformaticsrisk stratification

Identifiers

PMID40534992
PMCPMC12173735

What Socratic 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 Socratic graph.