ArticleLupus science & medicine2025
Application of machine learning in assessing disease activity in SLE.
Article in Lupus science & medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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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.
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Who cites it
1 citing paper in PubMed.
- Advancing Care in Lupus Nephritis: Expert Perspectives on Current Practices and Future Directions in Italy.Journal of clinical medicine · 2026Review
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSLE is a chronic autoimmune disease with immune complex deposition in various organs, causing inflammation. The Systemic Lupus Erythematosus Disease Activity Index 2000 assesses disease severity but is subjective. This study aimed to construct a machine learning model based on objective laboratory indicators to assess SLE disease activity.
methodsA retrospective study was conducted on 319 patients with SLE, collecting their clinical characteristics and laboratory indicators as model-building indicators. Multiple machine learning algorithms were employed to construct models for assessing SLE disease activity.
resultsThe patients were divided into two cohorts, cohort 1 used as the training set to build the machine learning models and cohort 2 for external validation. Six laboratory indicators, including anti-dsDNA (IFT), quantitative anti-dsDNA, neutrophils, globulin, proteinuria and NK cells, were selected to construct the SLE disease activity evaluation model. The XGBoost model demonstrated superior performance in distinguishing active SLE, with an area under the receiver operating characteristic curve of 0.934, accuracy of 0.925, sensitivity of 0.969, specificity of 0.750 and F1 score of 0.954.
conclusionsThis pioneering machine learning model, using objective laboratory indicators, enhances clinical feasibility and provides a novel method for assessing SLE disease activity, that may enable timely evaluation of SLE activity, facilitating preparation for treatment and prognosis.
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