Stroke recovery phenotyping through network trajectory approaches and graph neural networks.

ABSTRACT: Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of measures that may be continuous, ordinal, interval or categorical in nature, which can present challenges for multivariate regression approaches. This has hindered stroke researchers' ability to achieve an integrated picture of the complex time-evolving interactions among symptoms. Here, we use tools from network science and machine learning that are particularly well-suited to extracting underlying patterns in such data, and may assist in prediction of recovery patterns. To demonstrate the utility of this approach, we analyzed data from the NINDS tPA tri

SUBMITTER: Krishnagopal S 

PROVIDER: S-EPMC9206968 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature