A natural language processing algorithm accurately classifies steatotic liver disease pathology to estimate the risk of cirrhosis.

Journal: Hepatology communications
PMID: 38551386

Abstract

BACKGROUND: Histopathology remains the gold standard for diagnosing and staging metabolic dysfunction-associated steatotic liver disease (MASLD). The feasibility of studying MASLD progression in electronic medical records based on histological features is limited by the free-text nature of pathology reports. Here we introduce a natural language processing (NLP) algorithm to automatically score MASLD histology features.

Authors

  • Marc S Sherman
    Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Prasanna K Challa
    Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Eric M Przybyszewski
    Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Robert M Wilechansky
    Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Eugenia N Uche-Anya
    Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Ashley T Ott
    Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University, Stanford, California, USA.
  • Jessica McGoldrick
    Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Wolfram Goessling
    Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Hamed Khalili
    Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Tracey G Simon
    Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

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