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

Abstract

The machine-learning-based modeling is feasible for providing personalized risk prediction of sICH after stroke thrombolysis, and is able to reduce the CTT. More data are needed to further optimize the model and improve the accuracy of prediction.