A global effort to benchmark predictive models and reveal mechanistic diversity in long-term stroke outcomes.

ABSTRACT: Stroke remains a leading cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. To address these challenges, we established the NeuralCup consortium to benchmark predictive models of stroke outcome through a collaborative, data-driven approach. This study presents findings from 15 international teams who used a comprehensive dataset including clinical and imaging data, to identify and compare predictors of motor, cognitive, and emotional outcomes one year post-stroke. Our analyses integrated traditional statistical approaches and novel machine learning algorithms to uncover 'optimal recipes' for predicting each domain. The differences in these 'optimal recipes' ref

SUBMITTER: Matsulevits A 

PROVIDER: S-EPMC11507916 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature