Using machine learning to optimize selection of elderly patients for endovascular thrombectomy.

Journal: Journal of neurointerventional surgery
Published Date: Aug 1, 2019

Abstract

BACKGROUND: Endovascular thrombectomy (ET) is the standard of care for treatment of acute ischemic stroke (AIS) secondary to large vessel occlusion. The elderly population has been under-represented in clinical trials on ET, and recent studies have reported higher morbidity and mortality in elderly patients than in their younger counterparts.

Authors

  • Ali Alawieh
    Medical Scientist Training Program, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Fadi Zaraket
    Department of Electrical and Computer Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon.
  • Mohamed Baker Alawieh
    Department of Electrical and Computer Engineering, University of Texas, Austin, Texas, USA.
  • Arindam Rano Chatterjee
    Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Alejandro Spiotta
    Department of Neurosurgery, Medical University of South Carolina, Charleston, South Carolina, USA.

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