Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence.

ABSTRACT:

Background

Predicting treated language improvement (TLI) and transfer to the untreated language (cross-language generalization, CLG) after speech-language therapy in bilingual individuals with poststroke aphasia is crucial for personalized treatment planning. This study evaluated machine learning models to predict TLI and CLG and identified the key predictive features (eg, patient severity, demographics, and treatment variables) aligning with clinical evidence.

Methods

Forty-eight Spanish-English bilingual individuals with poststroke aphasia received 20 sessions of semantic feature-based naming treatment in either their first or second language. Comprehensive language, cognitive, and background bilingual experience assessments were administered pre- and post-treatment. Sixte

SUBMITTER: Marte MJ 

PROVIDER: S-EPMC11772114 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature