Machine Learning Identifies Clusters of Longitudinal Autoantibody Profiles Predictive of Systemic Lupus Erythematosus Disease Outcomes
Ann Rheum Dis 2023;82(7):927–36 doi 10.1136/ard-2022-223808
Choi, et al. used machine clustering techniques to divide SLE patients into four distinct clusters. This could potentially be used to predict future clinical outcomes, and as benchmarks to study other SLE-related outcomes.
Prior to this study, there have been attempts to stratify SLE patients into groups based on common antibody profiles, but none have used machine learning to study longitudinal antibody data in SLE.
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