Data · dataset · 2026
Application of Machine Learning Models Based on Systemic Immune Profiles in the Differential Diagnosis of Autoimmune Diseases
Listed in ScienceDB
Objective To explore the value of machine learning models based on peripheral blood immune profiles in the differential diagnosis of four autoimmune diseases (SLE, RA, pSS, AS) and non-autoimmune joint diseases.
Description
Methods
The multidimensional clinical data of 639 patients (525 with AIDs and 114 controls) were retrospectively analyzed. Six machine learning classifiers were constructed, and the SHAP algorithm was utilized to interpret the optimal model.Results Significant differences in immune characteristics were observed among the disease groups (P<0.05). The XGBoost model demonstrated the highest classification performance (AUC=0.998, accuracy=0.982).
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SHAP analysis extracted five core classification biomarkers, including Treg/CD4+(%), Treg(%), and anti-cardiolipin antibody IgG.Conclusions The XGBoost multi-class classification model based on systemic immune profiles exhibits high accuracy, holding promise as an objective tool for early auxiliary diagnosis in rheumatology.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.j00253.05010 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Medicine & Health · Social Science
- Inferred from text
- Disease 75% · Immunology 72%
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.j00253.05010 | 10 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[disease].local:disease:disease | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].anzsrc:group:3204 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (72%) |
| concepts[field].local:field:computer-science-ai | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |