Leveraging machine learning for enhanced and interpretable risk prediction of venous thromboembolism in acute ischemic stroke care.

Journal: PloS one
PMID: 40100876

Abstract

BACKGROUND: Venous thromboembolism (VTE) is a life-threatening complication commonly occurring after acute ischemic stroke (AIS), with an increased risk of mortality. Traditional risk assessment tools lack precision in predicting VTE in AIS patients due to the omission of stroke-specific factors.

Authors

  • Youli Jiang
    Department of Neurology, People's Hospital of Longhua, Shenzhen, China.
  • Ao Li
    Beijing University of Chinese Medicine, Beijing, China.
  • Zhihuan Li
    Center for Translational Innovations and Department of Respiratory and Critical Care Medicine, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China; Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China.
  • Yanfeng Li
    School of Electronic and Information Engineering, Beijing Jiaotong University, Shangyuan Village No 3 in Haidian, Beijing, China.
  • Rong Li
    Department of Neurology, People's Hospital of Longhua, Shenzhen, China.
  • Qingshi Zhao
    Department of Neurology, People's Hospital of Longhua, Shenzhen, China.
  • Guisu Li
    Department of Neurology, People's Hospital of Longhua, Shenzhen, China.

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