ArticleRMD open2021
Validation of a machine learning approach to estimate Systemic Lupus Erythematosus Disease Activity Index score categories and application in a real-world dataset.
Article in RMD open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis.Computational intelligence and neuroscience · 2022Pooled it
- Prediction of 1-Year Activity in Systemic Lupus Erythematosus: Hierarchical Machine Learning Approach.JMIR formative research · 2025Article
- Unveiling the Disparities in the Field of Precision Medicine: A Perspective.Health science reports · 2025Article
- Impact of a digital platform and flare risk blood biomarker index on lupus: A study protocol design for evaluating self efficacy and disease management.Contemporary clinical trials communications · 2025Article
- Application of machine learning in assessing disease activity in SLE.Lupus science & medicine · 2025Article
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Lupus Nephritis Risk Factors and Biomarkers: An Update.International journal of molecular sciences · 2023Review
- Application of Machine Learning Models in Systemic Lupus Erythematosus.International journal of molecular sciences · 2023Review
- Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions.The Journal of rheumatology · 2022Review
- Tailored treatment strategies and future directions in systemic lupus erythematosus.Rheumatology international · 2022Review
- Personalized Medicine and Machine Learning: A Roadmap for the Future.Journal of clinical medicine · 2022Article
- Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs).Therapeutic advances in musculoskeletal disease · 2022Review
- Validation of a machine learning approach to estimate Clinical Disease Activity Index Scores for rheumatoid arthritis.RMD open · 2021Article
- Validation of a machine learning approach to estimate expanded disability status scale scores for multiple sclerosis.Multiple sclerosis journal - experimental, translational and clinicalArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveUse of the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) in routine clinical practice is inconsistent, and availability of clinician-recorded SLEDAI scores in real-world datasets is limited. This study aimed to validate a machine learning model to estimate SLEDAI score categories using clinical notes and to apply the model to a large, real-world dataset to generate estimated score categories for use in future research studies.
methodsA machine learning model was developed to estimate an individual patient's SLEDAI score category (no activity, mild activity, moderate activity or high/very high activity) for a specific encounter date using clinical notes. A training cohort of 3504 encounters and a separate validation cohort of 1576 encounters were created from the OM1 SLE Registry. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calculated using a binarised version of the outcome that sets the positive class to be those records with clinician-recorded SLEDAI scores >5 and the negative class to be records with scores ≤5. Model performance was evaluated by categorising the scores into the four disease activity categories and by calculating the Spearman's R value and Pearson's R value.
resultsThe AUC for the two categories was 0.93 for the development cohort and 0.91 for the validation cohort. The model had a Spearman's R value of 0.7 and a Pearson's R value of 0.7 when calculated using the four disease activity categories.
conclusionThe model performs well when estimating SLEDAI score categories using unstructured clinical notes.
Indexed as
Identifiers
What OpenQuestion holds
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.