Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation.

ABSTRACT: Practicing clinicians in neurorehabilitation continue to lack a systematic evidence base to personalize rehabilitation therapies to individual patients and thereby maximize outcomes. Computational modeling- collecting, analyzing, and modeling neurorehabilitation data- holds great promise. A key question is how can computational modeling contribute to the evidence base for personalized rehabilitation? As representatives of the clinicians and clinician-scientists who attended the 2023 NSF DARE conference at USC, here we offer our perspectives and discussion on this topic. Our overarching thesis is that clinical insight should inform all steps of modeling, from construction to output, in neurorehabilitation and that this process requires close collaboration between researchers and the clinica

SUBMITTER: Lin DJ 

PROVIDER: S-EPMC10840185 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature