Identifying Metabolic Dysfunction-Associated Steatotic Liver Disease Using Natural Language Processing in a US National Cohort
An algorithm combining rule-based NLP with cardiometabolic risk factors and alcohol use from EHRs accurately identifies and classifies SLD and can be applied in large epidemiologic studies of SLD in the Veterans Health Administration.
Authors
- Catherine Mezzacappa (ORCID: https://orcid.org/0000-0001-7181-6902)
- Marina Serper (ORCID: https://orcid.org/0000-0003-4899-2160)
- Tamar H. Taddei (ORCID: https://orcid.org/0000-0002-6500-1739)
- Raphaella Ferreira (ORCID: https://orcid.org/0000-0003-4990-4839)
- Andreas Hentschel (ORCID: https://orcid.org/0000-0002-0272-9220)
- Bassam Dahman (ORCID: https://orcid.org/0000-0001-7209-5369)
- Dustin Bastaich
- Binu V. John (ORCID: https://orcid.org/0000-0002-2002-9682)
- Nadim Mahmud (ORCID: https://orcid.org/0000-0003-1889-3954)
- Andres Samos (ORCID: https://orcid.org/0000-0002-3682-4533)
- David E. Kaplan (ORCID: https://orcid.org/0000-0002-3839-336X)
Institutions
- University of Miami (US)
- Virginia Commonwealth University (US)
- Florida International University (US)
- Larkin Community Hospital (US)
- Yale University (US)
- VA Connecticut Healthcare System (US)
- University of Miami Health System (US)
- Philadelphia VA Medical Center (US)
- VA Central California Health Care System (US)
- Miami VA Healthcare System (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- The American Journal of Gastroenterology
- Published
- 2025-01-16
- DOI
- https://doi.org/10.14309/ajg.0000000000003321
- Citations
- 7
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 9.48