Personalized risk prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis using a machine-learning model.

ABSTRACT:

Background

Personalized prediction of the risk of symptomatic intracerebral hemorrhage (sICH) after stroke thrombolysis is clinically useful. Machine-learning-based modeling may provide the personalized prediction of the risk of sICH after stroke thrombolysis.

Methods

We identified 2578 thrombolysis-treated ischemic stroke patients between January 2013 and December 2016 from a multicenter database, where 70% were used to train models and the remaining 30% were used as the nominal test sets. Another 136 consecutive tissue plasminogen-activated-treated patients between January 2017 and December 2017 from our institute were enrolled as the independent test sets for clinical usability evaluation. Five machine-learning models were developed to predict the risk of sICH after strok

SUBMITTER: Wang F 

PROVIDER: S-EPMC8842114 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature