AIMC Topic: Language Therapy

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

Stroke
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...

Identifiable Patterns of Trait, State, and Experience in Chronic Stroke Recovery.

Neurorehabilitation and neural repair
BACKGROUND: Considerable evidence indicates that the functional connectome of the healthy human brain is highly stable, analogous to a fingerprint.

Identifying Children With Clinical Language Disorder: An Application of Machine-Learning Classification.

Journal of learning disabilities
In this study, we identified child- and family-level characteristics most strongly associated with clinical identification of language disorder for preschool-aged children. We used machine learning to identify variables that best classified children ...

ABCD: A Simulation Method for Accelerating Conversational Agents With Applications in Aphasia Therapy.

Journal of speech, language, and hearing research : JSLHR
PURPOSE: Development of aphasia therapies is limited by clinician shortages, patient recruitment challenges, and funding constraints. To address these barriers, we introduce (ABCD), a novel method for simulating goal-driven natural spoken dialogues ...