A semi-supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging.

Journal: Brain and behavior
PMID: 26807332

Abstract

INTRODUCTION: We developed a machine learning model to predict whether or not a cochlear implant (CI) candidate will develop effective language skills within 2 years after the CI surgery by using the pre-implant brain fMRI data from the candidate.

Authors

  • Lirong Tan
    Division of Biomedical Informatics Cincinnati Children's Hospital Research Foundation 3333 Burnet Avenue Cincinnati Ohio 45229; Department of Electrical Engineering and Computing System University of Cincinnati 812 Rhodes Hall Cincinnati Ohio 45221-0030.
  • Scott K Holland
    Pediatric Neuroimaging Research Consortium Cincinnati Children's Hospital Medical Center Cincinnati Ohio 45221.
  • Aniruddha K Deshpande
    Department of Speech-Language-Hearing-Sciences, 106A Davison Hall 110 Hofstra University, Hempstead New York 11549.
  • Ye Chen
    1 Department of Urology, First Affiliated Hospital of Soochow University, Suzhou, China.
  • Daniel I Choo
    Department of Otolaryngology College of Medicine University of Cincinnati Medical Sciences Building 231 Albert Sabin Way Cincinnati Ohio 45267.
  • Long J Lu
    Division of Biomedical Informatics Cincinnati Children's Hospital Research Foundation 3333 Burnet Avenue Cincinnati Ohio 45229; Department of Electrical Engineering and Computing System University of Cincinnati 812 Rhodes Hall Cincinnati Ohio 45221-0030; Department of Environmental Health College of Medicine University of Cincinnati 231 Albert Sabin Way Cincinnati Ohio 45267.

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