Leveraging Natural Language Processing to Accurately Identify Histologically Confirmed Esophageal and Gastric Premalignant and Malignant Conditions Using a Large-Scale Health-Record Linked Genomic Biobank
This study confirms the ability to use NLP on large-scale unstructured data linked to robust genetic and clinical data for future gastroesophageal precancer analyses and to inform targeted prevention and/or early detection interventions (eg, endoscopic surveillance).
Authors
- Shailja C. Shah (ORCID: https://orcid.org/0000-0002-2049-9959)
- Purushotham Karnam
- Andrew J. Gawron (ORCID: https://orcid.org/0000-0002-8968-6998)
- Kyriacos Markianos (ORCID: https://orcid.org/0000-0003-0214-6014)
- Saiju Pyarajan (ORCID: https://orcid.org/0000-0002-9047-3762)
- Samir Gupta (ORCID: https://orcid.org/0000-0002-4799-2348)
- Lin Liu (ORCID: https://orcid.org/0000-0001-9672-0042)
- Aaron Giera
Institutions
- Brigham and Women's Hospital (US)
- San Francisco VA Medical Center (US)
- Harvard University (US)
- University of Utah (US)
- Veterans Health Administration (US)
- VA Boston Healthcare System (US)
- University of California San Diego (US)
- VA Salt Lake City Healthcare System (US)
- Human Longevity (United States) (US)
Publication Details
- Journal
- Gastro Hep Advances
- Published
- 2025-08-26
- DOI
- https://doi.org/10.1016/j.gastha.2025.100768
- Primary Topic
- Esophageal Cancer Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00