Machine Learning for Predictive Analysis of Otolaryngology Residency Letters of Recommendation.

Journal: The Laryngoscope
PMID: 38602257

Abstract

INTRODUCTION: Letters of recommendation (LORs) are a highly influential yet subjective and often enigmatic aspect of the residency application process. This study hypothesizes that LORs do contain valuable insights into applicants and can be used to predict outcomes. This pilot study utilizes natural language processing and machine learning (ML) models using LOR text to predict interview invitations for otolaryngology residency applicants.

Authors

  • Vikram Vasan
    Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Christopher P Cheng
    Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • David K Lerner
    Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Karen Pascual
    Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
  • Amanda Mercado
    Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
  • Alfred Marc Iloreta
    Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Marita S Teng
    Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.

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