ArticleClinical rheumatology2020
Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis.
Article in Clinical rheumatology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi-center study.Headache · 2022Trial
- A Comprehensive Clinical Decision Support System for the Early Diagnosis of Axial Spondyloarthritis: Multi-Sequence MRI, Clinical Risk Integration, and Explainable Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- A study on the effectiveness of machine learning models for hepatitis prediction.Scientific reports · 2025Article
- Personalized dose reduction strategies for biologic disease-modifying antirheumatic drugs for treating axial spondyloarthritis: a clinical and economic evaluation with predictive modeling.BMC rheumatology · 2025Article
- 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
- Barriers and facilitators in diagnosing axial spondyloarthritis: a qualitative study.Rheumatology international · 2024Article
- Machine learning and deep learning for the diagnosis and treatment of ankylosing spondylitis- a scoping review.Journal of clinical orthopaedics and trauma · 2024Article
- Machine learning models with time-series clinical features to predict radiographic progression in patients with ankylosing spondylitis.Journal of rheumatic diseases · 2024Article
- Entry point of machine learning in axial spondyloarthritis.RMD open · 2024Review
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- Uncovering the burden of hidradenitis suppurativa misdiagnosis and underdiagnosis: a machine learning approach.Frontiers in medical technology · 2024Article
- Radiomic Quantification for MRI Assessment of Sacroiliac Joints of Patients with Spondyloarthritis.Journal of digital imaging · 2022Article
- Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs).Therapeutic advances in musculoskeletal disease · 2022Review
- An introduction to machine learning and analysis of its use in rheumatic diseases.Nature reviews. Rheumatology · 2021Review
- A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy.Nature communications · 2021Article
- Clinical features in primary care electronic records before diagnosis of ankylosing spondylitis: a nested case-control study.BMC family practice · 2020Article
- Application of machine learning in the diagnosis of axial spondyloarthritis.Current opinion in rheumatology · 2019Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectiveTo develop a predictive mathematical model for the early identification of ankylosing spondylitis (AS) based on the medical and pharmacy claims history of patients with and without AS.
methodsThis retrospective study used claims data from Truven databases from January 2006 to September 2015 (Segment 1) and October 2015 to February 2018 (Segment 2). Machine learning identified features differentiating patients with AS from matched controls; selected features were used as inputs in developing Model A/B to identify patients likely to have AS. Model A/B was trained and developed in Segment 1, and patients predicted to have AS in Segment 1 were followed up in Segment 2 to evaluate the predictive capability of Model A/B.
resultsOf 228,471 patients in Segment 1 without any history of AS, Model A/B predicted 1923 patients to have AS. Ultimately, 1242 patients received an AS diagnosis in Segment 2; 120 of these were correctly predicted by Model A/B, yielding a positive predictive value (PPV) of 6.24%. The diagnostic accuracy of Model A/B compared favorably with that of a clinical model (PPV, 1.29%) that predicted AS based on spondyloarthritis features described in the Assessment of SpondyloArthritis international Society classification criteria. A simplified linear regression model created to test the operability of Model A/B yielded a lower PPV (2.55%).
conclusionsModel A/B performed better than a clinically based model in predicting a diagnosis of AS among patients in a large claims database; its use may contribute to early recognition of AS and a timely diagnosis.
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