The potential recovery of post-stroke aphasia is highly variable and the rehabilitation
outcomes are difficult to predict. This interdisciplinary collaboration builds on data collected …

Predicting the potential recovery outcome of post-stroke aphasia remains a challenging task.
Our previous work [10] applied machine learning algorithms to predict participant response …

Background: Poststroke recovery depends on multiple factors and varies greatly across
individuals. Using machine learning models, this study investigated the independent and …

Our ability to make great progress in delivering, optimizing, and predicting rehabilitation
outcomes for individuals with aphasia is challenged by factors that influence rehabilitation …

The role of left and right hemisphere brain regions in language recovery after stroke-induced
aphasia remains controversial. Here, we summarize how neuroimaging studies increase the …

Objective To describe the trajectories of linguistic, cognitive-communicative, and health-
related quality of life (HRQOL) outcomes after stroke in persons with aphasia. Design …

Stroke can lead to debilitating consequences, including loss of language. An important goal
of stroke research is to use machine learning to predict outcomes and response to therapy …

Stroke is one of the leading causes of long-term disabilities in motor and cognition
functionality. An early and accurate prediction of rehabilitation outcomes can lead to a tailor …

The severity of post‐stroke aphasia and the potential for recovery are highly variable and
difficult to predict. Evidence suggests that optimal estimation of aphasia severity requires the …

Although several studies have aimed for accurate predictions of language recovery in post
stroke aphasia, individual language outcomes remain hard to predict. Large-scale prediction …