Utilizing natural language processing to improve abdominal aortic aneurysm surveillance
In the cohort, 39% of patients were compliant with recommended surveillance. Incorporating NLP into traditional screening and surveillance practices can allow providers to identify patients outside of recommended surveillance, bridging gaps in care.
Authors
- Matthew W. Mell (ORCID: https://orcid.org/0000-0002-5757-1985)
- Emily Xu
- Steven Maximus (ORCID: https://orcid.org/0000-0003-2819-1681)
- Kathryn DiLosa (ORCID: https://orcid.org/0000-0002-1984-5711)
- Misty D. Humphries (ORCID: https://orcid.org/0000-0001-7362-3303)
- Matthew Schneck (ORCID: https://orcid.org/0000-0002-3434-4552)
- Dena Sayrafi
- Aida Mushell
Institutions
- Michael E. DeBakey VA Medical Center (US)
- Baylor College of Medicine (US)
- University of California Davis Medical Center (US)
Publication Details
- Journal
- Journal of Vascular Surgery
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1016/j.jvs.2025.05.035
- Citations
- 1
- Primary Topic
- Aortic aneurysm repair treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.52