Deep Learning for Predictive Analysis of Pediatric Otolaryngology Personal Statements: A Pilot Study.

Journal: Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Published Date: Mar 8, 2022

Abstract

OBJECTIVE: The personal statement is often an underutilized aspect of pediatric otolaryngology fellowship applications. In this pilot study, we use deep learning language models to cluster personal statements and elucidate their relationship to applicant rank position and postfellowship research output.

Authors

  • Yeshwant Reddy Chillakuru
    Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Ave, San Francisco, CA, 94143, USA.
  • Diego A Preciado
    Sheikh Zayed Center for Pediatric Surgical Innovation and Division of Otolaryngology, Children's National Health System, Washington, DC, USA.
  • Jeremy Cha
    Sheikh Zayed Center for Pediatric Surgical Innovation and Division of Otolaryngology, Children's National Health System, Washington, DC, USA.
  • Hannah Mann
    Sheikh Zayed Center for Pediatric Surgical Innovation and Division of Otolaryngology, Children's National Health System, Washington, DC, USA.
  • Hengameh K Behzadpour
    Sheikh Zayed Center for Pediatric Surgical Innovation and Division of Otolaryngology, Children's National Health System, Washington, DC, USA.
  • Alexandra Genevieve Espinel
    Sheikh Zayed Center for Pediatric Surgical Innovation and Division of Otolaryngology, Children's National Health System, Washington, DC, USA.

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