Using machine learning models to predict post-revascularization thrombosis in PAD
Combining patient-specific characteristics with TEG-PM values in MLMs can effectively predict ATE following LER in PAD patients, enhancing high-risk patient identification and enabling tailored thromboprophylaxis.
Authors
- Anahita Dua
- Samir Ghandour (ORCID: https://orcid.org/0000-0001-6419-4447)
- Rahul Chaudhary
- Mounika Naidu Boya (ORCID: https://orcid.org/0000-0002-1635-1118)
- Adriana A Rodriguez Alvarez (ORCID: https://orcid.org/0000-0002-5394-3323)
- Isabella Ferlini Cieri (ORCID: https://orcid.org/0000-0002-6163-1055)
- Shiv Patel (ORCID: https://orcid.org/0009-0008-2580-436X)
- Anna Poucey
Institutions
- University of Pittsburgh (US)
- Massachusetts General Hospital (US)
- VA Pittsburgh Healthcare System (US)
- University of Pittsburgh Medical Center (US)
- Imperial College London (GB)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-05-07
- DOI
- https://doi.org/10.3389/frai.2025.1540503
- Citations
- 1
- Primary Topic
- Antiplatelet Therapy and Cardiovascular Diseases
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- National Institutes of Health