Predicting Functional Outcome Using 24-Hour Post-Treatment Characteristics: Application of Machine Learning Algorithms in the STRATIS Registry Article (Faculty180)

cited authors

  • Castonguay, Ali C; Zoghi, Zeinab; Zaidat, Osama O O; Burgess, Richard E; Zaidi, Syed F; Mueller-Kronast, Nils; Liebeskind, David S; Jumaa, Mouh A

description

  • 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-treatment characteristics in the Systematic Evaluation of Patients Treated With Neurothrombectomy Devices for Acute Ischemic Stroke (STRATIS) Registry.

authors

publication date

  • 2023

published in

start page

  • 40

end page

  • 49

volume

  • 93