Long-term forecasting of a motor outcome following rehabilitation in chronic stroke via a hierarchical bayesian dynamic model

BACKGROUND: Given the heterogeneity of stroke, it is important to determine the best course of motor therapy for each patient, i.e., to personalize rehabilitation based on predictions of long-term outcomes. Here, we propose a hierarchical Bayesian dynamic (i.e., state-space) model (HBDM) to forecast...

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Detalles Bibliográficos
Autores principales: Schweighofer, Nicolas, Ye, Dongze, Luo, Haipeng, D’Argenio, David Z., Winstein, Carolee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311775/
https://www.ncbi.nlm.nih.gov/pubmed/37386512
http://dx.doi.org/10.1186/s12984-023-01202-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311775/
https://www.ncbi.nlm.nih.gov/pubmed/37386512
http://dx.doi.org/10.1186/s12984-023-01202-y

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