ArticleBMC primary care2022
Detection of primary Sjögren's syndrome in primary care: developing a classification model with the use of routine healthcare data and machine learning.
Article in BMC primary care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Prediction Models for Primary Care, Based on AI and Electronic Health Records: Systematic Review.JMIR medical informatics · 2025Pooled it
- Use of machine learning for chronic disease case identification in primary care settings: A scoping review of methods, validation, and clinical relevance.The Journal of international medical research · 2026Article
- Predicting major organ complications in primary Sjögren's disease using a machine learning ensemble strategy: a dual-center retrospective clinical study.Frontiers in immunology · 2026Article
- Diagnostic methods for managing dry eyes.World journal of methodology · 2025Article
- A multi-criterion feature integration framework for accurate diagnosis of Sjögren's disease using routine laboratory tests.NPJ digital medicine · 2025Article
- Early detection of G2SCH through machine learning analysis of physical examination metrics.NPJ digital medicine · 2025Article
- Current imaging applications, radiomics, and machine learning modalities of CNS demyelinating disorders and its mimickers.Journal of neurology · 2025Review
- Implications of Data Extraction and Processing of Electronic Health Records for Epidemiological Research: Observational Study.Journal of medical Internet research · 2025Observational
- Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Prediction of Sjögren's disease diagnosis using matched electronic dental-health record data.BMC medical informatics and decision making · 2024Article
- Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases.Lupus science & medicine · 2024Article
- Reliability of non-contact tongue diagnosis for Sjögren's syndrome using machine learning method.Scientific reports · 2023Article
- Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrimary Sjögren's Syndrome (pSS) is a rare autoimmune disease that is difficult to diagnose due to a variety of clinical presentations, resulting in misdiagnosis and late referral to specialists. To improve early-stage disease recognition, this study aimed to develop an algorithm to identify possible pSS patients in primary care. We built a machine learning algorithm which was based on combined healthcare data as a first step towards a clinical decision support system.
methodRoutine healthcare data, consisting of primary care electronic health records (EHRs) data and hospital claims data (HCD), were linked on patient level and consisted of 1411 pSS and 929,179 non-pSS patients. Logistic regression (LR) and random forest (RF) models were used to classify patients using age, gender, diseases and symptoms, prescriptions and GP visits.
resultsThe LR and RF models had an AUC of 0.82 and 0.84, respectively. Many actual pSS patients were found (sensitivity LR = 72.3%, RF = 70.1%), specificity was 74.0% (LR) and 77.9% (RF) and the negative predictive value was 99.9% for both models. However, most patients classified as pSS patients did not have a diagnosis of pSS in secondary care (positive predictive value LR = 0.4%, RF = 0.5%).
conclusionThis is the first study to use machine learning to classify patients with pSS in primary care using GP EHR data. Our algorithm has the potential to support the early recognition of pSS in primary care and should be validated and optimized in clinical practice. To further enhance the algorithm in detecting pSS in primary care, we suggest it is improved by working with experienced clinicians.
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