Discover important donor-recipient risk factors and interactions in heart transplant primary graft dysfunction with machine learning
In summary, ML has promising applications in PGD prediction as a computational tool for clinical study. We can also use the ML model to help us identify and discover new risk factors and interactions between donor and recipient.
Authors
- Sirui Ding
- Yafen Liang (ORCID: https://orcid.org/0000-0001-7902-7836)
- Cheryl L. Brown
- Xiaoqian Jiang (ORCID: https://orcid.org/0000-0001-9933-2205)
- Xia Hu (ORCID: https://orcid.org/0000-0003-2234-3226)
- Na Zou (ORCID: https://orcid.org/0000-0003-1984-795X)
- Chia-Yuan Chang (ORCID: https://orcid.org/0009-0001-1889-612X)
Institutions
- University of North Carolina at Charlotte (US)
- University of Houston (US)
- Rice University (US)
- Texas A&M University (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-04-28
- DOI
- https://doi.org/10.1093/jamia/ocaf066
- Primary Topic
- Transplantation: Methods and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health