Predicting Functional Outcome Using 24‐Hour Post‐Treatment Characteristics: Application of Machine Learning Algorithms in the STRATIS Registry

SUMMARY FOR SOCIAL MEDIA: @AliciaCastongu2, @FazalZaidi9, @oozaidat, @Mouhammad_Jumaa OBJECTIVE: Machine learning (ML) algorithms have emerged as powerful predictive tools in the field stroke. Here, we examine the predictive accuracy of ML models for predicting functional outcomes using 24‐hour post...

Descripción completa

Detalles Bibliográficos
Autores principales: Castonguay, Alicia C., Zoghi, Zeinab, Zaidat, Osama O., Burgess, Richard E., Zaidi, Syed F., Mueller‐Kronast, Nils, Liebeskind, David S., Jumaa, Mouhammad A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10091739/
https://www.ncbi.nlm.nih.gov/pubmed/36214566
http://dx.doi.org/10.1002/ana.26528

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10091739/
https://www.ncbi.nlm.nih.gov/pubmed/36214566
http://dx.doi.org/10.1002/ana.26528

Ejemplares similares