56 Using machine learning to predict 30-day readmission and reoperation following resection of supratentorial high-grade gliomas: A national analysis of 9,418 patients.

56 Using machine learning to predict 30-day readmission and reoperation following resection of supratentorial high-grade gliomas: A national analysis of 9,418 patients.

OBJECTIVES/GOALS: High-grade gliomas (HGG) are among the rarest, most aggressive tumors in neurosurgical practice. We aimed to identify the clinical predictors for 30-day readmission and reoperation following HGGs surgery using the NSQIP database and seek to create web-based applications predicting...

Full description

Bibliographic Details
Main Authors: Abdul Karim Ghaith, Marc Ghanem, Cameron Zamanian, Antonio A. Bon Nieves, Archis Bhandarkar, Karim Nathani, Mohamad Bydon, Alfredo Quiones-Hinojosa
Format: Article
Language:English
Published: Cambridge University Press 2023-04-01
Series:Journal of Clinical and Translational Science
Online Access:https://www.cambridge.org/core/product/identifier/S2059866123001449/type/journal_article

Internet

https://www.cambridge.org/core/product/identifier/S2059866123001449/type/journal_article

Similar Items