Trial reportBMC nephrology2022
Artificial neural network - an effective tool for predicting the lupus nephritis outcome.
Trial report in BMC nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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
12 citing papers in PubMed, 15 citations in OpenAlex.
- Machine learning models identify prognostic factors in systemic lupus erythematosus patients with epstein-barr virus infection.Clinical rheumatology · 2026Article
- Use of Artificial Intelligence in Rheumatoid Arthritis: Advancements and Novel Perspectives.Journal of clinical medicine · 2026Review
- Applications of artificial intelligence in systemic lupus erythematosus: integrating multi-omics data for precision medicine.Frontiers in immunology · 2026Review
- Repeat kidney biopsy in pediatric lupus nephritis: an analysis of associated clinical-pathological features and outcomes.Frontiers in pediatrics · 2026Article
- Machine learning in lupus nephritis: bridging prediction models and clinical decision-making towards personalized nephrology.Frontiers in medicine · 2025Review
- Prevalence and risk factors of osteoporosis in lupus nephritis patients in China: a cross-sectional study.BMC nephrology · 2024Article
- Article
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Systemic Immune Inflammation Index as a Key Predictor of Dialysis in Pediatric Chronic Kidney Disease with the Use of Random Forest Classifier.Journal of clinical medicine · 2023Article
- Investigation of factors influencing low physical activity levels in community-dwelling older adults with chronic pain: a cross-sectional study.Scientific reports · 2023Article
- The Artificial Neural Network as a Diagnostic Tool of the Risk ofJournal of clinical medicine · 2023Article
- Application of Machine Learning Models in Systemic Lupus Erythematosus.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLupus nephropathy (LN) occurs in approximately 50% of patients with systemic lupus erythematosus (SLE), and 20% of them will eventually progress into end-stage renal disease (ESRD). A clinical tool predicting remission of proteinuria might be of utmost importance. In our work, we focused on predicting the chance of complete remission achievement in LN patients, using artificial intelligence models, especially an artificial neural network, called the multi-layer perceptron.
methodsIt was a single centre retrospective study, including 58 individuals, with diagnosed systemic lupus erythematous and biopsy proven lupus nephritis. Patients were assigned into the study cohort, between 1st January 2010 and 31st December 2020, and eventually randomly allocated either to the training set (N = 46) or testing set (N = 12). The end point was remission achievement. We have selected an array of variables, subsequently reduced to the optimal minimum set, providing the best performance.
resultsWe have obtained satisfactory results creating predictive models allowing to assess, with accuracy of 91.67%, a chance of achieving a complete remission, with a high discriminant ability (AUROC 0.9375).
conclusionOur solution allows an accurate assessment of complete remission achievement and monitoring of patients from the group with a lower probability of complete remission. The obtained models are scalable and can be improved by introducing new patient records.
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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.