ArticleFrontiers in medicine2022
Machine learning-based improvement of an online rheumatology referral and triage system.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 19 citations in OpenAlex.
- RhePort 1.3 enhances early identification of inflammatory rheumatic diseases: a prospective study in German rheumatology settings.Rheumatology international · 2025Trial
- Diagnostic Accuracy of a Mobile AI-Based Symptom Checker and a Web-Based Self-Referral Tool in Rheumatology: Multicenter Randomized Controlled Trial.Journal of medical Internet research · 2024Trial
- Machine learning improves online self-referral for inflammatory rheumatic diseases: a registry-based validation study.Rheumatology international · 2026Article
- Low-energy small language models with retrieval-augmented generation can surpass large-model performance in rheumatology.Frontiers in medicine · 2026Article
- Development and validation of a clinical risk prediction rule to identify inflammatory arthritis at the point of rheumatology triage.Rheumatology advances in practice · 2026Article
- Optimizing people's movement across the health system: a scoping review of referral systems within a primary health care approach.Primary health care research & development · 2025Article
- Referral patterns, influencing factors, and satisfaction related to referrals of patients with rheumatic diseases to other healthcare professionals: an online survey of rheumatologists.Rheumatology international · 2025Article
- Mapping and Summarizing the Research on AI Systems for Automating Medical History Taking and Triage: Scoping Review.Journal of medical Internet research · 2025Article
- Early detection of rheumatoid arthritis through patient empowerment by tailored digital monitoring and education: a feasibility study.Rheumatology international · 2025Article
- Design and implementation of a radiomic-driven intelligent dental hospital diversion system utilizing multilabel imaging data.Journal of translational medicine · 2024Article
- Unveiling Artificial Intelligence's Power: Precision, Personalization, and Progress in Rheumatology.Journal of clinical medicine · 2024Review
- Prioritising Appointments by Telephone Interview: Duration from Symptom Onset to Appointment Request Predicts Likelihood of Inflammatory Rheumatic Disease.Journal of clinical medicine · 2024Article
- [Rheumatological care in Germany : Memorandum of the German Society for Rheumatology and Clinical Immunology 2024].Zeitschrift fur Rheumatologie · 2024Article
- Prediction of the acceptance of telemedicine among rheumatic patients: a machine learning-powered secondary analysis of German survey data.Rheumatology international · 2024Article
- Stepwise asynchronous telehealth assessment of patients with suspected axial spondyloarthritis: results from a pilot study.Rheumatology international · 2024Article
- "The Simpler, the Better." A Qualitative Study on Digital Health Transformation in Early Adopter Rheumatology Outpatient Clinics.Inquiry : a journal of medical care organization, provision and financingArticle
Corrections and comments
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Authors and funding
15 authors at 10 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Rheport is an online rheumatology referral system allowing automatic appointment triaging of new rheumatology patient referrals according to the respective probability of an inflammatory rheumatic disease (IRD). Previous research reported that Rheport was well accepted among IRD patients. Its accuracy was, however, limited, currently being based on an expert-based weighted sum score. This study aimed to evaluate whether machine learning (ML) models could improve this limited accuracy. Materials and methods: Data from a national rheumatology registry (RHADAR) was used to train and test nine different ML models to correctly classify IRD patients. Diagnostic performance was compared of ML models and the current algorithm was compared using the area under the receiver operating curve (AUROC). Feature importance was investigated using shapley additive explanation (SHAP). Results: A complete data set of 2265 patients was used to train and test ML models. 30.5% of patients were diagnosed with an IRD, 69.3% were female. The diagnostic accuracy of the current Rheport algorithm (AUROC of 0.534) could be improved with all ML models, (AUROC ranging between 0.630 and 0.737). Targeting a sensitivity of 90%, the logistic regression model could double current specificity (17% vs. 33%). Finger joint pain, inflammatory marker levels, psoriasis, symptom duration and female sex were the five most important features of the best performing logistic regression model for IRD classification. Conclusion: In summary, ML could improve the accuracy of a currently used rheumatology online referral system. Including further laboratory parameters and enabling individual feature importance adaption could increase accuracy and lead to broader usage.
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