AutoML accurately predicts endovascular mechanical thrombectomy in acute large vessel ischemic stroke.

ABSTRACT:

Background and objective

Automated machine learning or autoML has been widely deployed in various industries. However, their adoption in healthcare, especially in clinical settings is constrained due to a lack of clear understanding and explainability. The aim of this study is to utilize autoML for the prediction of functional outcomes in patients who underwent mechanical thrombectomy and compare it with traditional ML models with a focus on the explainability of the trained models.

Methods

A total of 156 patients of acute ischemic stroke with Large Vessel Occlusion (LVO) who underwent mechanical thrombectomy within 24 h of stroke onset were included in the study. A total of 34 treatment variables including clinical, demographic, imaging, and procedure-related data were extr

SUBMITTER: Raj R 

PROVIDER: S-EPMC10569475 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature