Development of a Natural Language Processing Model for Extracting Kidney Biopsy Pathology Diagnoses.

Journal: Kidney medicine
Published Date: Jun 14, 2025

Abstract

RATIONALE & OBJECTIVE: Kidney biopsy reports are in a nonindexed text format, and the diagnosis requires labor-intensive manual abstraction. Natural language processing (NLP) has not been rigorously tested for kidney biopsy diagnosis extraction. Our objective was to develop an accurate model to extract the biopsy diagnosis from free-text reports.

Authors

  • Shane A Bobart
    Division of Nephrology, Hypertension and Transplantation, Houston Methodist Hospital, Houston, TX.
  • Enshuo Hsu
    McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Thomas Potter
    Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA.
  • Luan Truong
    Department of Pathology and Genomic Medicine, The Houston Methodist Hospital and Research Institute, Houston, TX, USA.
  • Amy Waterman
    J.C. Walter Jr Transplant Center, Houston Methodist Hospital, Houston, TX.
  • Stephen Jones
    Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
  • Tariq Shafi
    Division of Nephrology, The University of Mississippi Medical Center, Jackson, MS, USA.

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