Evidence map›Paper›PMID 40528978›Full record

ArticleFundamental research2025

Machine learning-based prediction models for atopic dermatitis diagnosis and evaluation.

Songjiang Wu, Li Lei, Yibo Hu, Ling Jiang, Chuhan Fu, Yushan Zhang, Lu Zhu, Jinhua Huang, Jing Chen, Qinghai Zeng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Fundamental research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. ADAM19: biological characteristics and implications in disease pathology and clinical applications.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026
    Review
  2. Epithelial-Dermal Immune Memory: TrackingInternational journal of molecular sciences · 2026
    Review
  3. Article
  4. Review
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

10 authors.

Songjiang WuDepartment of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
Li LeiDepartment of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
Yibo HuDepartment of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
Ling JiangDepartment of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
Chuhan FuDepartment of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
Yushan ZhangDepartment of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
Lu ZhuDepartment of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
Jinhua HuangDepartment of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
Jing ChenDepartment of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
Qinghai ZengDepartment of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Atopic dermatitis (AD) is the most common chronic inflammatory skin disease seriously affecting the quality of life of patients. Reliable and accurate evaluation methods are necessary for early diagnosis and effective AD treatment. Thus, this study used machine learning (ML) to explore a novel diagnostic and therapeutic effect evaluation model for AD. Firstly, candidate model genes were screened from an integrated data set of four AD-related microarray datasets: GSE133477, GSE32924, GSE58558, and GSE107361, using Robust Rank Aggregation (RRA), and protein-protein interaction network (PPI). Next, three recognized models (REC) and three AD-associated gene models (AAG) established with LASSO, Logistic linear regression (LR), and random forest (RF) were developed and tested separately using GSE130588 and GSE99802 datasets. The results revealed that REC model of LASSO (model genes including

Indexed as

Atopic dermatitisEffects evaluationImmune infiltrationMachine learningPrediction model

Identifiers

PMID40528978
PMCPMC12167871

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.