Machine learning-based prediction of symptomatic intracerebral hemorrhage after intravenous thrombolysis for stroke: a large multicenter study

Machine learning-based prediction of symptomatic intracerebral hemorrhage after intravenous thrombolysis for stroke: a large multicenter study

BackgroundThis study aimed to compare the performance of different machine learning models in predicting symptomatic intracranial hemorrhage (sICH) after thrombolysis treatment for ischemic stroke.MethodsThis multicenter study utilized the Shenyang Stroke Emergency Map database, comprising 8,924 acu...

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Bibliographic Details
Main Authors: Rui Wen, Miaoran Wang, Wei Bian, Haoyue Zhu, Ying Xiao, Qian He, Yu Wang, Xiaoqing Liu, Yangdi Shi, Zhe Hong, Bing Xu
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-10-01
Series:Frontiers in Neurology
Subjects:
machine learning
prediction
symptomatic intracerebral hemorrhage
intravenous thrombolysis
stroke
multicenter
Online Access:https://www.frontiersin.org/articles/10.3389/fneur.2023.1247492/full

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https://www.frontiersin.org/articles/10.3389/fneur.2023.1247492/full

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