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Original research
Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests

Authors

  • Jianan Wang Department of Neurology, Zhejiang University School of Medicine Second Affiliated Hospital Department of Neurology, Hangzhou, Zhejiang, China PubMed articlesGoogle scholar articles
  • Jungen Zhang Department of Neurology, Zhejiang University School of Medicine Second Affiliated Hospital Department of Neurology, Hangzhou, Zhejiang, China PubMed articlesGoogle scholar articles
  • Xiaoxian Gong Department of Neurology, Zhejiang University School of Medicine Second Affiliated Hospital Department of Neurology, Hangzhou, Zhejiang, China PubMed articlesGoogle scholar articles
  • Wenhua Zhang Department of Neurology, Zhejiang University School of Medicine Second Affiliated Hospital Department of Neurology, Hangzhou, Zhejiang, China PubMed articlesGoogle scholar articles
  • Ying Zhou Department of Neurology, Zhejiang University School of Medicine Second Affiliated Hospital Department of Neurology, Hangzhou, Zhejiang, China PubMed articlesGoogle scholar articles
  • Min Lou Department of Neurology, Zhejiang University School of Medicine Second Affiliated Hospital Department of Neurology, Hangzhou, Zhejiang, China PubMed articlesGoogle scholar articles
  1. Correspondence to Dr Min Lou; lm99{at}zju.edu.cn
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Citation

Wang J, Zhang J, Gong X, et al
Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests
Stroke and Vascular Neurology 2022;7:e001096. doi: 10.1136/svn-2021-001096

Publication history

  • Received May 3, 2021
  • Accepted September 27, 2021
  • First published October 26, 2021.
Online issue publication 
July 07, 2022

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© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.