Prediction of 1-Year Activity in Systemic Lupus Erythematosus: Hierarchical Machine Learning Approach.

ABSTRACT: Systemic lupus erythematosus (SLE) is a chronic disease characterized by a broad spectrum of involved organs, including neurological, renal, and vascular domains, with disease activity manifesting through unpredictable patterns that vary across individuals and over time, making the prediction of activity events particularly challenging. This paper proposes a hierarchical machine learning model to predict a 12-month SLE activity, defined as the occurrence of at least one event among SLE hospitalization, new organ-involved domain, and neurological, renal, or vascular manifestation within the following year. At each patient's visit, the model considers all the features at the current time point, the information about the patient's clinical history, and about its last 12 months, to predict the

SUBMITTER: Lilli L 

PROVIDER: S-EPMC12373299 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature