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Original research
An explainable machine learning model for predicting the outcome of ischemic stroke after mechanical thrombectomy

Authors

  • Zhelv Yao Department of Neurology, Nanjing University Medical School Affiliated Nanjing Drum Tower Hospital, Nanjing, Jiangsu, China Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China Nanjing Medicine Center For Neurological Diseases, Nanjing, China PubMed articlesGoogle scholar articles
  • Chenglu Mao Department of Neurology, Nanjing University Medical School Affiliated Nanjing Drum Tower Hospital, Nanjing, Jiangsu, China Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China Nanjing Medicine Center For Neurological Diseases, Nanjing, China PubMed articlesGoogle scholar articles
  • Zhihong Ke Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China Nanjing Medicine Center For Neurological Diseases, Nanjing, China Department of Neurology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China PubMed articlesGoogle scholar articles
  • Yun Xu Department of Neurology, Nanjing University Medical School Affiliated Nanjing Drum Tower Hospital, Nanjing, Jiangsu, China Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China Nanjing Medicine Center For Neurological Diseases, Nanjing, China PubMed articlesGoogle scholar articles
  1. Correspondence to Dr Yun Xu, Neurology, Nanjing University Medical School Affiliated Nanjing Drum Tower Hospital, Nanjing, China; xuyun20042001{at}aliyun.com
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Citation

Yao Z, Mao C, Ke Z, et al
An explainable machine learning model for predicting the outcome of ischemic stroke after mechanical thrombectomy

Publication history

  • Received September 3, 2022
  • Accepted November 1, 2022
  • First published November 29, 2022.
Online issue publication 
October 12, 2023
  • Supplementary Data

    This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

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