ReviewThe Korean journal of internal medicine2019
Application of machine learning in rheumatic disease research.
Review in The Korean journal of internal medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 4 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
24 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Sex bias consideration in healthcare machine-learning research: a systematic review in rheumatoid arthritis.BMJ open · 2025Pooled it
- Predictive models for acute kidney injury in acute pancreatitis: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Factors predicting treatment response to biological and targeted synthetic disease-modifying antirheumatic drugs in psoriatic arthritis - a systematic review and meta-analysis.Clinical rheumatology · 2024Pooled it
- Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis.Computational intelligence and neuroscience · 2022Pooled it
- IMPACT: Integrated Model For Patient Acuity, Care, And Triage Efficiency In Emergency Department.Journal of medical systems · 2026Article
- Comparative Value of Traditional versus Novel Immune-Inflammatory Indices in Assessing Disease Activity of Sjögren's Disease: A Multicenter Cohort Study.Clinical rheumatology · 2026Article
- A Novel Machine Learning Approach to Assist Early Diagnosis of Diffuse Panbronchiolitis.Journal of Korean medical science · 2025Article
- Predictive models for posttransplant diabetes mellitus in kidney transplant recipients using machine learning and deep learning approach: a nationwide cohort study from South Korea.Kidney research and clinical practice · 2025Article
- A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation.Journal of medical Internet research · 2025Article
- Performance of the systemic lupus erythematosus risk probability index (SLERPI) in the Egyptian college of rheumatology (ECR) study cohort.Clinical rheumatology · 2025Article
- The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence.Rheumatology international · 2024Review
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Prediction of the acceptance of telemedicine among rheumatic patients: a machine learning-powered secondary analysis of German survey data.Rheumatology international · 2024Article
- Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review.Rheumatology international · 2023Review
- Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting.Arthritis research & therapy · 2023Article
- Observational
- The development of a web-based app employing machine learning for delirium prevention in long-term care facilities in South Korea.BMC medical informatics and decision making · 2022Review
- Fast machine learning annotation in the medical domain: a semi-automated video annotation tool for gastroenterologists.Biomedical engineering online · 2022Article
- An introduction to machine learning and analysis of its use in rheumatic diseases.Nature reviews. Rheumatology · 2021Review
- Complex Machine-Learning Algorithms and Multivariable Logistic Regression on Par in the Prediction of Insufficient Clinical Response to Methotrexate in Rheumatoid Arthritis.Journal of personalized medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past decade, there has been a paradigm shift in how clinical data are collected, processed and utilized. Machine learning and artificial intelligence, fueled by breakthroughs in high-performance computing, data availability and algorithmic innovations, are paving the way to effective analyses of large, multi-dimensional collections of patient histories, laboratory results, treatments, and outcomes. In the new era of machine learning and predictive analytics, the impact on clinical decision-making in all clinical areas, including rheumatology, will be unprecedented. Here we provide a critical review of the machine-learning methods currently used in the analysis of clinical data, the advantages and limitations of these methods, and how they can be leveraged within the field of rheumatology.
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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.