ArticleSeminars in arthritis and rheumatism2011
Validation of psoriatic arthritis diagnoses in electronic medical records using natural language processing.
Article in Seminars in arthritis and rheumatism, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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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
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature.Frontiers in immunology · 2022Pooled it
- Extracting information from the text of electronic medical records to improve case detection: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2016Pooled it
- Improving case definition of Crohn's disease and ulcerative colitis in electronic medical records using natural language processing: a novel informatics approach.Inflammatory bowel diseases · 2013Trial
- Article
- Excess cardiovascular morbidity in psoriatic arthritis and cardioprotective effects of biologic dmards: a propensity-matched analysis.Scientific reports · 2026Article
- One Step Closer to Conversational Medical Records: ChatGPT Parses Psoriasis Treatments from EMRs.Journal of clinical medicine · 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
- AI in Psoriatic Disease: Scoping Review.JMIR dermatology · 2024Article
- Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases.Life (Basel, Switzerland) · 2024Review
- Advancing rheumatology with natural language processing: insights and prospects from a systematic review.Rheumatology advances in practice · 2024Review
- Clinical Burden of Concomitant Joint Disease in Psoriasis: A US-Linked Claims and Electronic Health Records Database Analysis.Advances in therapy · 2021Article
- Leveraging unstructured data to identify hereditary angioedema patients in electronic medical records.Allergy, asthma, and clinical immunology : official journal of the Canadian Society of Allergy and Clinical Immunology · 2021Article
- Article
- Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients.Nature communications · 2018Article
- Development and validation of a heart failure with preserved ejection fraction cohort using electronic medical records.BMC cardiovascular disorders · 2018Article
- Surrogate-assisted feature extraction for high-throughput phenotyping.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Web-based Real-Time Case Finding for the Population Health Management of Patients With Diabetes Mellitus: A Prospective Validation of the Natural Language Processing-Based Algorithm With Statewide Electronic Medical Records.JMIR medical informatics · 2016Article
- Does Inflammation Mediate the Association Between Obesity and Insulin Resistance?Inflammation · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectivesTo test whether data extracted from full text patient visit notes from an electronic medical record would improve the classification of psoriatic arthritis (PsA) compared with an algorithm based on codified data.
methodsFrom the >1,350,000 adults in a large academic electronic medical record, all 2318 patients with a billing code for PsA were extracted and 550 were randomly selected for chart review and algorithm training. Using codified data and phrases extracted from narrative data using natural language processing, 31 predictors were extracted and 3 random forest algorithms were trained using coded, narrative, and combined predictors. The receiver operator curve was used to identify the optimal algorithm and a cut-point was chosen to achieve the maximum sensitivity possible at a 90% positive predictive value (PPV). The algorithm was then used to classify the remaining 1768 charts and finally validated in a random sample of 300 cases predicted to have PsA.
resultsThe PPV of a single PsA code was 57% (95% CI 55%-58%). Using a combination of coded data and natural language processing (NLP), the random forest algorithm reached a PPV of 90% (95% CI 86%-93%) at a sensitivity of 87% (95% CI 83%-91%) in the training data. The PPV was 93% (95% CI 89%-96%) in the validation set. Adding NLP predictors to codified data increased the area under the receiver operator curve (P < 0.001).
conclusionsUsing NLP with text notes from electronic medical records improved the performance of the prediction algorithm significantly. Random forests were a useful tool to accurately classify psoriatic arthritis cases to enable epidemiological research.
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