ArticleRheumatology (Oxford, England)2020
Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records.
Article in Rheumatology (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Exploring digital health technology in rheumatology: a scoping review on benefits, challenges, and future possibilities.EULAR rheumatology open · 2026Review
- Development of a confidence-based Natural Language Processing tool to identify inflammatory arthritis from non-inflammatory conditions in Rheumatology medical notes.Arthritis research & therapy · 2026Article
- Natural language processing improves reliable identification of COVID-19 compared to diagnostic codes alone.American journal of epidemiology · 2025Article
- Review
- Large language models and rheumatology: are we there yet?Rheumatology advances in practice · 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
- RAGing ahead in rheumatology: new language model architectures to tame artificial intelligence.Therapeutic advances in musculoskeletal disease · 2025Review
- Rheumatology in the digital health era: status quo and quo vadis?Nature reviews. Rheumatology · 2024Review
- The association of TNF inhibitor use with incident cardiovascular events in radiographic axial spondyloarthritis.Seminars in arthritis and rheumatism · 2024Article
- Extracting patient lifestyle characteristics from Dutch clinical text with BERT models.BMC medical informatics and decision making · 2024Article
- Natural language processing to identify and characterize spondyloarthritis in clinical practice.RMD open · 2024Observational
- Barriers and facilitators in diagnosing axial spondyloarthritis: a qualitative study.Rheumatology international · 2024Article
- Entry point of machine learning in axial spondyloarthritis.RMD open · 2024Review
- Improving the accuracy of automated gout flare ascertainment using natural language processing of electronic health records and linked Medicare claims data.Pharmacoepidemiology and drug safety · 2024Article
- Advancing rheumatology with natural language processing: insights and prospects from a systematic review.Rheumatology advances in practice · 2024Review
- Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives.Pain research & management · 2023Review
- 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
- Artificial Intelligence and Deep Learning for Rheumatologists.Arthritis & rheumatology (Hoboken, N.J.) · 2022Review
- Natural Language Processing to Improve Prediction of Incident Atrial Fibrillation Using Electronic Health Records.Journal of the American Heart Association · 2022Article
- Radiomic Quantification for MRI Assessment of Sacroiliac Joints of Patients with Spondyloarthritis.Journal of digital imaging · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectivesTo develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Classification of Diseases (ICD) codes.
methodsAn enriched cohort of 7853 eligible patients was created from electronic health records of two large hospitals using automated searches (⩾1 ICD codes combined with simple text searches). Key disease concepts from free-text data were extracted using NLP and combined with ICD codes to develop algorithms. We created both supervised regression-based algorithms-on a training set of 127 axSpA cases and 423 non-cases-and unsupervised algorithms to identify patients with high probability of having axSpA from the enriched cohort. Their performance was compared against classifications using ICD codes only.
resultsNLP extracted four disease concepts of high predictive value: ankylosing spondylitis, sacroiliitis, HLA-B27 and spondylitis. The unsupervised algorithm, incorporating both the NLP concept and ICD code for AS, identified the greatest number of patients. By setting the probability threshold to attain 80% positive predictive value, it identified 1509 axSpA patients (mean age 53 years, 71% male). Sensitivity was 0.78, specificity 0.94 and area under the curve 0.93. The two supervised algorithms performed similarly but identified fewer patients. All three outperformed traditional approaches using ICD codes alone (area under the curve 0.80-0.87).
conclusionAlgorithms incorporating free-text data can accurately identify axSpA patients in electronic health records. Large cohorts identified using these novel methods offer exciting opportunities for future clinical research.
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