Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy.

Journal: Computers in biology and medicine
Published Date: Jun 19, 2015

Abstract

BACKGROUND: This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervised learning methods in patients with drug-resistant focal seizures suspected to begin in temporal lobe.

Authors

  • Negar Memarian
    Department of Psychology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States. Electronic address: nmemarian@ucla.edu.
  • Sally Kim
    Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States.
  • Sandra Dewar
    Department of Neurosurgery, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States.
  • Jerome Engel
    Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Neurosurgery, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Neurobiology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States.
  • Richard J Staba
    Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States.

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