Machine learning-based Cerebral Venous Thrombosis diagnosis with clinical data.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Published Date: Jul 2, 2024

Abstract

OBJECTIVES: Cerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT relies on early diagnosis. Our study focuses on developing a machine learning-based screening algorithm using clinical data from a large neurology referral center in southern Iran.

Authors

  • Ali Namjoo-Moghadam
    Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Vida Abedi
    Department of Public Health Science, Penn State College of Medicine, Hershey, PA, USA.
  • Venkatesh Avula
    Department of Molecular and Functional Genomics, Geisinger Health System, Danville, USA.
  • Nahid Ashjazadeh
    Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Etrat Hooshmandi
    Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Niloufar Abedinpour
    Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Zahra Rahimian
    School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Afshin Borhani-Haghighi
    Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Hunter Medical Research Institute and University of Newcastle, Newcastle, Australia. Electronic address: neuro.ab@gmail.com.
  • Ramin Zand
    Neuroscience Institute, Geisinger Health System, 100 North Academy Ave, Danville, PA 17822, USA.

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