ArticleArthritis research & therapy2021
Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics.
Article in Arthritis research & therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 64 citations in OpenAlex.
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
- Machine learning for predicting treatment response to biologic and targeted synthetic disease-modifying antirheumatic drugs in rheumatoid arthritis: a scoping review.BMC rheumatology · 2025Pooled it
- Toward causal artificial intelligence for biologic treatment response in rheumatoid arthritis: current evidence and future directions.Rheumatology international · 2026Review
- Bridging the gap: combining treat-to-target and difficult-to-treat strategies in the management of rheumatoid arthritis.Nature reviews. Rheumatology · 2026Review
- Inferring Rheumatoid Arthritis Disease Activity Status From the Electronic Health Records Across Health Systems.Arthritis & rheumatology (Hoboken, N.J.) · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Predicting early discontinuation of adalimumab in patients with rheumatoid arthritis using machine learning: A specialty pharmacy-based approach.Journal of managed care & specialty pharmacy · 2026Article
- Computational approaches to multimodal data integration in rheumatoid arthritis: from data landscape to clinical translation.Briefings in bioinformatics · 2026Review
- Machine learning-assisted screening of clinical features for predicting difficult-to-treat rheumatoid arthritis.Scientific reports · 2025Article
- Serum metabolomics identifies unique inflammatory signatures to distinguish rheumatoid arthritis responders and non-responders to TNF inhibitor therapy.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Artificial intelligence to predict treatment response in rheumatoid arthritis and spondyloarthritis: a scoping review.Rheumatology international · 2025Article
- Applications of AI in Predicting Drug Responses for Type 2 Diabetes.JMIR diabetes · 2025Article
- Associations between age, red cell distribution width and 180-day and 1-year mortality in giant cell arteritis patients: mediation analyses and machine learning in a cohort study.Arthritis research & therapy · 2025Article
- Predicting abatacept retention using machine learning.Arthritis research & therapy · 2025Observational
- Exploring the relationship between per- and polyfluoroalkyl substances exposure and rheumatoid arthritis risk using interpretable machine learning.Frontiers in public health · 2025Article
- Machine learning-based prediction of response to Janus kinase inhibitors in patients with rheumatoid arthritis using clinical data.Frontiers in immunology · 2025Article
- Development and evaluation of a multivariable prediction model for clinical improvement in an established cohort of Colombian rheumatoid arthritis patients.Therapeutic advances in musculoskeletal disease · 2025Article
- Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients.Bioengineering (Basel, Switzerland) · 2024Article
- Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis.Journal of clinical medicine · 2024Article
- Prediction of ineffectiveness of biological drugs using machine learning and explainable AI methods: data from the Austrian Biological Registry BioReg.Arthritis research & therapy · 2024Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe developed a model to predict remissions in patients treated with biologic disease-modifying anti-rheumatic drugs (bDMARDs) and to identify important clinical features associated with remission using explainable artificial intelligence (XAI).
methodsWe gathered the follow-up data of 1204 patients treated with bDMARDs (etanercept, adalimumab, golimumab, infliximab, abatacept, and tocilizumab) from the Korean College of Rheumatology Biologics and Targeted Therapy Registry. Remission was predicted at 1-year follow-up using baseline clinical data obtained at the time of enrollment. Machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost) were used for the predictions. The Shapley additive explanation (SHAP) value was used for interpretability of the predictions.
resultsThe ranges for accuracy and area under the receiver operating characteristic of the newly developed machine learning model for predicting remission were 52.8-72.9% and 0.511-0.694, respectively. The Shapley plot in XAI showed that the impacts of the variables on predicting remission differed for each bDMARD. The most important features were age for adalimumab, rheumatoid factor for etanercept, erythrocyte sedimentation rate for infliximab and golimumab, disease duration for abatacept, and C-reactive protein for tocilizumab, with mean SHAP values of - 0.250, - 0.234, - 0.514, - 0.227, - 0.804, and 0.135, respectively.
conclusionsOur proposed machine learning model successfully identified clinical features that were predictive of remission in each of the bDMARDs. This approach may be useful for improving treatment outcomes by identifying clinical information related to remissions in patients with rheumatoid arthritis.
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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.