Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study.

Journal: BMC medical research methodology
PMID: 39256656

Abstract

BACKGROUND: The prognosis, recurrence rates, and secondary prevention strategies varied significantly among different subtypes of acute ischemic stroke (AIS). Machine learning (ML) techniques can uncover intricate, non-linear relationships within medical data, enabling the identification of factors associated with etiological classification. However, there is currently a lack of research utilizing ML algorithms for predicting AIS etiology.

Authors

  • Siding Chen
    Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing 100070, China.
  • Xiaomeng Yang
    Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
  • Hongqiu Gu
    Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing 100070, China.
  • Yanzhao Wang
    School of Statistics, Renmin University of China, No. 59 Zhongguancun Street, Haidian District, Beijing, 100872, China.
  • Zhe Xu
    Thayer School of Engineering at Dartmouth College Hanover NH USA john.zhang@dartmouth.edu.
  • Yong Jiang
    Department of Pathology West China Hospital Sichuan University Chengdu China.
  • Yongjun Wang
    Department of Neurology, Beijing Tiantan Hospital, Beijing, China.

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