Forecasting Achievement of Inactive Disease in Juvenile Idiopathic Arthritis with Artificial Intelligence.

ABSTRACT: Objective: to seek for predictors of inactive disease (ID) in juvenile idiopathic arthritis (JIA) with artificial intelligence. Methods: The clinical charts of patients seen within 6 months after disease onset between 2007 and 2019 and with follow-up visits at 6, 12, 18, and 24 months were reviewed retrospectively. Sixty-eight potential predictors were recorded at each visit. The primary endpoint was ID at 24 months by 2004 Wallace criteria. Data obtained from diverse combinations of visits were examined to identify the best forecasting model. After pre-processing, the cohort was divided into training (50%) and testing (50%) cohorts. Multivariate time series forecasting, coupled with the Random Forest method, was used to train the machine learning (ML) forecasting model. Pred

SUBMITTER: Rebollo-Gimenez AI 

PROVIDER: S-EPMC12191878 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature