Relation Detection to Identify Stroke Assertions from Clinical Notes Using Natural Language Processing.

Journal: Studies in health technology and informatics
Published Date: Jan 25, 2024

Abstract

According to the World Stroke Organization, 12.2 million people world-wide will have their first stroke this year almost half of which will die as a result. Natural Language Processing (NLP) may improve stroke phenotyping; however, existing rule-based classifiers are rigid, resulting in inadequate performance. We report findings from a pilot study using NLP to improve relation detection for stroke assertion detection to support research studies and healthcare operations.

Authors

  • Audrey Yang
    University of Pennsylvania, Philadelphia, PA, USA.
  • Sam Kamien
    University of Pennsylvania, Philadelphia, PA, USA.
  • Anahita Davoudi
    Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA.
  • Sy Hwang
    Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania.
  • Meet Gandhi
    University of Pennsylvania, Philadelphia, PA, USA.
  • Ryan Urbanowicz
    University of Pennsylvania, Institute for Biomedical Informatics, Philadelphia, PA.
  • Danielle Mowery
    Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, 84108 UT United States.

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