JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information.

Journal: AJR. American journal of roentgenology
Published Date: Oct 24, 2018

Abstract

OBJECTIVE: When treatment decisions are being made for patients with acute ischemic stroke, timely and accurate outcome prediction plays an important role. The optimal rehabilitation strategy also relies on long-term outcome predictions. The decision-making process involves numerous biomarkers including imaging features and demographic information. The objective of this study was to integrate common stroke biomarkers using machine learning methods and predict patient recovery outcome at 90 days.

Authors

  • Yuan Xie
  • Bin Jiang
    Department of Urology, Chinese People's Liberation Army General Hospital, Beijing, 100039 China.
  • Enhao Gong
    Department of Electrical Engineering, Stanford University, Stanford, California, USA.
  • Ying Li
    School of Information Engineering, Chang'an University, Xi'an 710010, China.
  • Guangming Zhu
    1 Department of Radiology, Neuroradiology Division, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.
  • Patrik Michel
    3 Department of Radiology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
  • Max Wintermark
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Greg Zaharchuk
    Stanford University, Stanford CA 94305, USA.

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