ArticleScientific reports2017
Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sex bias consideration in healthcare machine-learning research: a systematic review in rheumatoid arthritis.BMJ open · 2025Pooled it
- Machine learning-based prediction of 30-day mortality in critically ill patients with rheumatoid arthritis.Clinical rheumatology · 2026Article
- Explainable machine learning-based mortality prediction in critically ill patients with rheumatoid arthritis-associated lung disease: a radiomics and clinical data integration study.BMC medical imaging · 2026Article
- Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.Journal of biomedical science · 2025Review
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases.Lupus science & medicine · 2024Article
- Nomogram for predicting post-therapy recurrence in BCLC A/B hepatocellular carcinoma with Child-Pugh B cirrhosis.Frontiers in immunology · 2024Article
- Meniscus injury prediction model based on metric learning.PeerJ. Computer science · 2024Article
- 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
- Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and therapy · 2022Review
- Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs).Therapeutic advances in musculoskeletal disease · 2022Review
- A novel prognostic score to assess the risk of progression in relapsing-remitting multiple sclerosis patients.European journal of neurology · 2021Article
- Machine Learning in Rheumatic Diseases.Clinical reviews in allergy & immunology · 2021Review
- The Challenges of Telemedicine in Rheumatology.Frontiers in medicine · 2021Review
- The Digital Way to Intercept Psoriatic Arthritis.Frontiers in medicine · 2021Review
- Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis.Frontiers in pharmacology · 2021Review
- Deep Learning Classification of Systemic Sclerosis Skin Using the MobileNetV2 Model.IEEE open journal of engineering in medicine and biology · 2021Article
- Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging.Rheumatology international · 2020Review
- Machine learning to predict early TNF inhibitor users in patients with ankylosing spondylitis.Scientific reports · 2020Article
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
We developed and independently validated a rheumatoid arthritis (RA) mortality prediction model using the machine learning method Random Survival Forests (RSF). Two independent cohorts from Madrid (Spain) were used: the Hospital Clínico San Carlos RA Cohort (HCSC-RAC; training; 1,461 patients), and the Hospital Universitario de La Princesa Early Arthritis Register Longitudinal study (PEARL; validation; 280 patients). Demographic and clinical-related variables collected during the first two years after disease diagnosis were used. 148 and 21 patients from HCSC-RAC and PEARL died during a median follow-up time of 4.3 and 5.0 years, respectively. Age at diagnosis, median erythrocyte sedimentation rate, and number of hospital admissions showed the higher predictive capacity. Prediction errors in the training and validation cohorts were 0.187 and 0.233, respectively. A survival tree identified five mortality risk groups using the predicted ensemble mortality. After 1 and 7 years of follow-up, time-dependent specificity and sensitivity in the validation cohort were 0.79-0.80 and 0.43-0.48, respectively, using the cut-off value dividing the two lower risk categories. Calibration curves showed overestimation of the mortality risk in the validation cohort. In conclusion, we were able to develop a clinical prediction model for RA mortality using RSF, providing evidence for further work on external validation.
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