Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction.

ABSTRACT:

Background and purpose

Mechanical thrombectomy greatly improves stroke outcomes. Nonetheless, some patients fall short of full recovery despite good reperfusion. The purpose of this study was to develop machine learning (ML) models for the pre-interventional prediction of functional outcome at 3 months of thrombectomy in acute ischemic stroke (AIS), using clinical and auto-extractable radiological information consistently available upon first emergency evaluation.

Materials and methods

A two-center retrospective cohort of 293 patients with AIS who underwent thrombectomy was analyzed. ML models were developed to predict dichotomized modified Rankin score at 90 days (mRS-90) using clinical and imaging features, both separately and combined. Conventional and experimental imagin

SUBMITTER: Jabal MS 

PROVIDER: S-EPMC9160988 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature