ReviewCurrent opinion in rheumatology2019
Application of machine learning in the diagnosis of axial spondyloarthritis.
Review in Current opinion in rheumatology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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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.
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Who cites it
17 citing papers in PubMed, 36 citations in OpenAlex.
- Early diagnosis of axial spondyloarthritis in primary care using multi-agent systems.NPJ digital medicine · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection model.Annals of medicine · 2025Article
- A study on the effectiveness of machine learning models for hepatitis prediction.Scientific reports · 2025Article
- Machine learning discrimination of Gleason scores below GG3 and above GG4 for HSPC patients diagnosis.Scientific reports · 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
- Construction and verification of atopic dermatitis diagnostic model based on pyroptosis related biological markers using machine learning methods.BMC medical genomics · 2023Article
- A Diagnostic Classifier Based on Circulating miRNA Pairs for COPD Using a Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Using natural language processing to explore characteristics and management of patients with axial spondyloarthritis and psoriatic arthritis treated under real-world conditions in Spain: SpAINET study.Therapeutic advances in musculoskeletal disease · 2023Article
- The application of machine learning in early diagnosis of osteoarthritis: a narrative review.Therapeutic advances in musculoskeletal disease · 2023Review
- Predicting a diagnosis of ankylosing spondylitis using primary care health records-A machine learning approach.PloS one · 2023Article
- A glance into the future of diagnosis and treatment of spondyloarthritis.Therapeutic advances in musculoskeletal disease · 2022Review
- An introduction to machine learning and analysis of its use in rheumatic diseases.Nature reviews. Rheumatology · 2021Review
- Machine Learning in Rheumatic Diseases.Clinical reviews in allergy & immunology · 2021Review
- A Three-Gene Peripheral Blood Potential Diagnosis Signature for Acute Rejection in Renal Transplantation.Frontiers in molecular biosciences · 2021Article
- Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewIn this review article, we describe the development and application of machine-learning models in the field of rheumatology to improve the detection and diagnosis rates of underdiagnosed rheumatologic conditions, such as ankylosing spondylitis and axial spondyloarthritis (axSpA). RECENT
findingsIn an attempt to aid in the earlier diagnosis of axSpA, we developed machine-learning models to predict a diagnosis of ankylosing spondylitis and axSpA using administrative claims and electronic medical record data. Machine-learning algorithms based on medical claims data predicted the diagnosis of ankylosing spondylitis better than a model developed based on clinical characteristics of ankylosing spondylitis. With additional clinical data, machine-learning algorithms developed using electronic medical records identified patients with axSpA with 82.6-91.8% accuracy. These two algorithms have helped us understand potential opportunities and challenges associated with each data set and with different analytic approaches. Efforts to refine and validate these machine-learning models are ongoing. SUMMARY: We discuss the challenges and benefits of machine-learning models in healthcare, along with potential opportunities for its application in the field of rheumatology, particularly in the early diagnosis of axSpA and ankylosing spondylitis.
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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.