Predictive models for secondary epilepsy in patients with acute ischemic stroke within one year.

Journal: eLife
PMID: 39540824

Abstract

BACKGROUND: Post-stroke epilepsy (PSE) is a critical complication that worsens both prognosis and quality of life in patients with ischemic stroke. An interpretable machine learning model was developed to predict PSE using medical records from four hospitals in Chongqing.

Authors

  • Jinxin Liu
    Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA, United States.
  • Haoyue He
    Department of Neurosurgery, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China.
  • Yanglingxi Wang
    Department of Neurosurgery, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China.
  • Jun Du
    Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214062, P.R. China.
  • Kaixin Liang
    Department of Psychology, Faculty of Social Sciences, University of Macau, Macau, China.
  • Jun Xue
    Department of Echocardiography, China Meitan General Hospital, Beijing, China.
  • Yidan Liang
    Department of Neurosurgery, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China.
  • Peng Chen
  • Shanshan Tian
    Department of Prehospital Emergency, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China.
  • Yongbing Deng
    Department of Neurosurgery, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China.

Keywords

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