Can machine learning improve on the early prediction of upper limb recovery after stroke?

ABSTRACT:

Background

Early prediction of upper limb recovery is important to optimise rehabilitation and inform patients but remains challenging due to inter-individual variability. This study aims to (1) develop and validate a machine learning model to predict arm-hand capacity at six months post-stroke using clinical variables from the first week; (2) compare its performance to a mixed-effects model; and (3) co-design a user-friendly output visualisation with clinician input.

Methods

From data of 451 first-ever ischemic stroke patients, we selected total Action Research Arm Test score (ARAT), shoulder abduction, and finger extension as predictors. An XGBoost model was trained on these variables measured at varying time points within the first five months, using 5-fold, 5-repeat cros

SUBMITTER: van der Gun GJ 

PROVIDER: S-EPMC12557897 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature