Identification of hepatic steatosis among persons with and without HIV using natural language processing.

Journal: Hepatology communications
PMID:

Abstract

BACKGROUND: Steatotic liver disease (SLD) is a growing phenomenon, and our understanding of its determinants has been limited by our ability to identify it clinically. Natural language processing (NLP) can potentially identify hepatic steatosis systematically within large clinical repositories of imaging reports. We validated the performance of an NLP algorithm for the identification of SLD in clinical imaging reports and applied this tool to a large population of people with and without HIV.

Authors

  • Jessie Torgersen
    Department of Medicine, Penn Center for AIDS Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Melissa Skanderson
    Connecticut VA Healthcare System, West Haven, CT, USA.
  • Farah Kidwai-Khan
    Yale School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA. Electronic address: farah.kidwai-khan@yale.edu.
  • Dena M Carbonari
    Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Center for Real-world Effectiveness and Safety of Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Janet P Tate
    VA Connecticut Healthcare System, West Haven, CT, USA.
  • Lesley S Park
    Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, California, USA.
  • Debika Bhattacharya
    VA Greater Los Angeles Healthcare System and David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
  • Joseph K Lim
    Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
  • Tamar H Taddei
    Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
  • Amy C Justice
    Department of Internal Medicine, Yale University School of Medicine, New Haven.
  • Vincent Lo Re
    Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.