Validation of a Zero-shot Learning Natural Language Processing Tool to Facilitate Data Abstraction for Urologic Research.

Journal: European urology focus
Published Date: Jan 25, 2024

Abstract

BACKGROUND: Urologic research often requires data abstraction from unstructured text contained within the electronic health record. A number of natural language processing (NLP) tools have been developed to aid with this time-consuming task; however, the generalizability of these tools is typically limited by the need for task-specific training.

Authors

  • Basil Kaufmann
    Department of Urology University Hospital Zurich Zurich Switzerland.
  • Dallin Busby
    Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY.
  • Chandan Krushna Das
    Milton and Carroll Petrie Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Neeraja Tillu
    Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Mani Menon
  • Ashutosh K Tewari
    School of Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C.
  • Michael A Gorin
    Milton and Carroll Petrie Department of Urology Icahn School of Medicine at Mount Sinai New York New York USA.

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