Natural language processing to intercept rheumatoid and psoriatic arthritis from clinical notes in the Emergency Department

作者：A. Tonutti, Pierandrea Morandini, Cosimo Faeti, Nicoletta Luciano, S. D'amico, Antonio Voza, V. Savevski, Carlo Selmi · 发表于：Therapeutic Advances in Musculoskeletal Disease · 年份：2026 · DOI：10.1177/1759720X261431361 · 被引用次数：1 · 研究领域：Medicine

Background: Underdiagnosis and delayed diagnosis of rheumatoid (RA) and psoriatic arthritis (PsA) remain major concerns, causing untimely treatments and impacting outcomes. Objective: We hypothesize that natural language processing (NLP) may identify red flags from clinical notes in non-specialist settings during early disease phases, thereby improving patient referral. Design and methods: Patients diagnosed with RA or PsA were retrospectively reviewed. Clinical notes from Emergency Department visits occurring in the 12 months prior to the diagnosis were analyzed through NLP into a final classification layer. Propensity score-matched controls without arthritis accessing the same Emergency Department were selected for comparison. Results: Among 650 patients with inflammatory arthritis seeking emergency evaluations in the 12 months before the diagnosis, 294 (45.2%) had PsA and 356 (54.8%) had RA. NLP achieved modest performance in training (AUCROC 70% for RA and 69% for PsA) with a marked drop in independent test sets (AUCROC 62% and 61%, respectively), suggesting limited robustness and accuracy for routine clinical use. Further analysis suggested an overlap between terms included in notes of cases classified correctly versus incorrectly. Manual selection of notes suggestive of arthritis by Rheumatologists did not improve the model performance, which remained of limited generalizability. NLP identified demographic and clinical peculiarities of RA and PsA patients presenting to ...