ArticlePloS one2017
Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Artificial Intelligence-based detection of neuropsychiatric lupus: an exploratory meta-analysis of neuroimaging and multimodal biomarker models.Clinical and experimental medicine · 2026Pooled it
- Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis.Computational intelligence and neuroscience · 2022Pooled it
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- Prediction of 1-Year Activity in Systemic Lupus Erythematosus: Hierarchical Machine Learning Approach.JMIR formative research · 2025Article
- How artificial intelligence can enable personalized mesenchymal stem cell-based therapeutic strategies in systemic lupus erythematosus.Frontiers in immunology · 2025Review
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Machine learning-based remission prediction in rheumatoid arthritis patients treated with biologic disease-modifying anti-rheumatic drugs: findings from the Kuwait rheumatic disease registry.Frontiers in big data · 2024Article
- Application of Machine Learning Models in Systemic Lupus Erythematosus.International journal of molecular sciences · 2023Review
- Artificial intelligence in glomerular diseases.Pediatric nephrology (Berlin, Germany) · 2022Review
- Article
- Adverse pregnancy outcomes in women with systemic lupus erythematosus: can we improve predictions with machine learning?Lupus science & medicine · 2022Article
- Tailored treatment strategies and future directions in systemic lupus erythematosus.Rheumatology international · 2022Review
- The Impacts of the Clinical and Genetic Factors on Chronic Damage in Caucasian Systemic Lupus Erythematosus Patients.Journal of clinical medicine · 2022Article
- Development and Verify of Survival Analysis Models for Chinese Patients With Systemic Lupus Erythematosus.Frontiers in immunology · 2022Article
- Identification and Prediction of Chronic Diseases Using Machine Learning Approach.Journal of healthcare engineering · 2022Article
- An introduction to machine learning and analysis of its use in rheumatic diseases.Nature reviews. Rheumatology · 2021Review
- Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks.BMC medical informatics and decision making · 2021Article
- Machine Learning in Rheumatic Diseases.Clinical reviews in allergy & immunology · 2021Review
- Deep Learning Classification of Systemic Sclerosis Skin Using the MobileNetV2 Model.IEEE open journal of engineering in medicine and biology · 2021Article
- Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges.Frontiers in pharmacology · 2021Review
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe increased survival in Systemic Lupus Erythematosus (SLE) patients implies the development of chronic damage, occurring in up to 50% of cases. Its prevention is a major goal in the SLE management. We aimed at predicting chronic damage in a large monocentric SLE cohort by using neural networks.
methodsWe enrolled 413 SLE patients (M/F 30/383; mean age ± SD 46.3±11.9 years; mean disease duration ± SD 174.6 ± 112.1 months). Chronic damage was assessed by the SLICC/ACR Damage Index (SDI). We applied Recurrent Neural Networks (RNNs) as a machine-learning model to predict the risk of chronic damage. The clinical data sequences registered for each patient during the follow-up were used for building and testing the RNNs.
resultsAt the first visit in the Lupus Clinic, 35.8% of patients had an SDI≥1. For the RNN model, two groups of patients were analyzed: patients with SDI = 0 at the baseline, developing damage during the follow-up (N = 38), and patients without damage (SDI = 0). We created a mathematical model with an AUC value of 0.77, able to predict damage development. A threshold value of 0.35 (sensitivity 0.74, specificity 0.76) seemed able to identify patients at risk to develop damage.
conclusionWe applied RNNs to identify a prediction model for SLE chronic damage. The use of the longitudinal data from the Sapienza Lupus Cohort, including laboratory and clinical items, resulted able to construct a mathematical model, potentially identifying patients at risk to develop damage.
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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.