Prediction of longitudinal outcomes and novel cluster identification in epilepsy.

ABSTRACT: The longitudinal course of epilepsy remains largely unpredictable. This study aimed to predict final outcome and classify dynamic longitudinal trajectories using artificial intelligence. A total of 2586 patients who first visited our epilepsy specialists between 2008 and 2017 and with at least 3 years of follow-up, were retrospectively enrolled. Supervised and unsupervised learning algorithms were employed to identify clusters with distinct longitudinal courses and to examine epilepsy parameters within each cluster. XGBoost showed slightly higher performance than the others for the final outcome prediction. We identified three clusters associated with final seizure freedom and two clusters with persistent seizures. The first cluster demonstrated early remission, often linked to infectious

SUBMITTER: Park KI 

PROVIDER: S-EPMC12660973 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature