Groupes de patients identifiés par machine learning à partir d'une analyse groupée du programme de développement clinique du sécukinumab dans le rhumatisme psoriasique, la spondylarthrite ankylosante et le rhumatisme psoriasique avec manifestations axiales
Clin Exp Rheumatol 2023;42(3):696–701 doi: 10.55563/clinexprheumatol/b8co74
Psoriatic arthritis clusters, obtained by machine learning (ML) analysis of pooled data from the FUTURE, MEASURE, and MAXIMISE trials, indicate phenotypical heterogeneity of patients with PsA and axial manifestations and overlapping features across the spondyloarthritis spectrum. Here, Baraliakos, et al. sort to identify distinct clinical clusters, based on patient demographics and baseline clinical indicators, from the secukinumab clinical development programme.
ML techniques can help identify distinct clusters of patients with potential therapeutic or prognostic significance, leading to a better understanding of disease and evolution towards precision medicine.
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