B-079 Identifying Heterogeneity of Systemic Connective Tissue Diseases by Applying Joint Dimension Reduction and Cluster Analysis to Immunomarkers
نویسندگان
چکیده
Abstract Background Accurate identification of systemic connective tissue diseases (SCTDs) can be challenging due to the heterogeneity each disease. This study aims explore different SCTDs by applying non-supervised learning immunomarker data and proposes an alternative classification. Additionally, investigates clinical implications these groups. Methods We utilized multiple correspondence analysis k-means clustering analyze patients with lupus erythematosus (SLE), rheumatoid arthritis (RA), Sjögren’s syndrome (SS) in Taiwan between 2001 2016. To investigate correlations classification results, we conducted a comprehensive review findings compared results across Results A total 11 923 three were classified into six distinct clusters based on their patterns. In this system, RA predominantly grouped cluster 1, whereas SLE SS had more dispersed distribution. Among SLE, renal involvement was frequent 3 6 (52% 51%, respectively) than other clusters, discoid 3. patients, typical such as inflammation or infection eyes allergic reactions common 2 (54% respectively). contrast, unspecified benign neoplasms (58%) likely experience immune disorders (63%). Conclusion The immunomarker-driven yields result new insights. data-driven approach would provide evidence-based aid accurate diagnosis management for SCTDs.
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ژورنال
عنوان ژورنال: Clinical Chemistry
سال: 2023
ISSN: ['0009-9147', '1530-8561']
DOI: https://doi.org/10.1093/clinchem/hvad097.418