Explainable machine learning for long-term outcome prediction in two-center stroke patients after intravenous thrombolysis.

ABSTRACT:

Objective

Neurological outcome prediction in patients with ischemic stroke is very critical in treatment strategy and post-stroke management. Machine learning techniques with high accuracy are increasingly being developed in the medical field. We studied the application of machine learning models to predict long-term neurological outcomes in patients with after intravenous thrombolysis.

Methods

A retrospective cohort study was performed to review all stroke patients with intravenous thrombolysis. Patients with modified Rankin Score (mRs) less than two at three months post-thrombolysis were considered as good outcome. The clinical features between stroke patients with good and with poor outcomes were compared using three different machine learning models (Random Forest, Suppo

SUBMITTER: Ping Z 

PROVIDER: S-EPMC9992421 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature