Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.

ABSTRACT:

Introduction

Acute ischemic stroke (AIS) patients often experience poor functional outcomes post-intravenous thrombolysis (IVT). Novel computational methods leveraging machine learning (ML) architectures increasingly support medical decision-making. We aimed to develop and validate a machine learning model to predict 3-month unfavorable functional outcome after IVT in AIS patients.

Methods

This retrospective study developed ML prognostic models for 3-month functional outcome (modified Rankin scale scores of 3-6) in IVT-treated AIS patients. A derivation cohort (n = 938) was split 7:3 for training/testing, with an independent external validation cohort (n = 324). The least absolute shrinkage and selection operator (LASSO) regression selected predictors from clin

SUBMITTER: Bu F 

PROVIDER: S-EPMC12510848 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature