ReviewInternational journal of molecular sciences2023
Application of Machine Learning Models in Systemic Lupus Erythematosus.
Review in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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.
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.
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The application of artificial intelligence in systemic lupus erythematosus: a bibliometric analysis of current trends and future directions.Frontiers in medicine · 2026Pooled it
- Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- Unsupervised machine learning identifies distinct SLE patient endotypes with differential response to belimumab.Rheumatology (Oxford, England) · 2025Trial
- An explainable machine learning model for predicting in-hospital infection in patients with systemic lupus erythematosus.Renal failure · 2026Article
- Mitigating bias in multilabel medical text classification: a cooperative training framework with dynamic debiasing.Bioinformatics (Oxford, England) · 2026Article
- From pathogenesis to precision medicine in systemic lupus erythematosus: Emerging biomarkers and targeted interventions.iScience · 2026Review
- Comprehensive analysis of predictive models for disease manifestations and case fatality in systemic lupus erythematosus.NPJ digital medicine · 2026Review
- A Narrative Review on Integrative Bioinformatics Approaches for microRNA Research in Familial Mediterranean Fever: Current Insights and Future Directions.Health science reports · 2026Article
- Machine Learning for MRI Classification of Systemic Lupus Erythematous Patients with and without Neuropsychiatric Events.Journal of imaging informatics in medicine · 2026Article
- Developing a prediction model for poor prognosis in MPA patients using initial admission examination results: a machine learning study from Southwest China.Frontiers in immunology · 2026Article
- Male Systemic Lupus Erythematosus: A Rare Case of Multisystem Involvement.International medical case reports journal · 2026Article
- Exploring the Immunomodulatory Role of Forkhead Box Protein 3 (FOXP3) in the Pathophysiology of Neuropsychiatric Disorders.Molecular neurobiology · 2025Review
- Data-Driven Cluster Analysis of Cerebrospinal Fluid Proteome and Associations with Clinical Phenotypes in Systemic Lupus Erythematosus.ACR open rheumatology · 2025Article
- Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records.BioData mining · 2024Article
- Helios as a Potential Biomarker in Systemic Lupus Erythematosus and New Therapies Based on Immunosuppressive Cells.International journal of molecular sciences · 2023Review
- An interpretable machine learning pipeline based on transcriptomics predicts phenotypes of lupus patients.iScience · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease and is extremely heterogeneous in terms of immunological features and clinical manifestations. This complexity could result in a delay in the diagnosis and treatment introduction, with impacts on long-term outcomes. In this view, the application of innovative tools, such as machine learning models (MLMs), could be useful. Thus, the purpose of the present review is to provide the reader with information about the possible application of artificial intelligence in SLE patients from a medical perspective. To summarize, several studies have applied MLMs in large cohorts in different disease-related fields. In particular, the majority of studies focused on diagnosis and pathogenesis, disease-related manifestations, in particular Lupus Nephritis, outcomes and treatment. Nonetheless, some studies focused on peculiar features, such as pregnancy and quality of life. The review of published data demonstrated the proposal of several models with good performance, suggesting the possible application of MLMs in the SLE scenario.
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Registered trials
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.