Machine Learning for 1‐Year Graft Failure Prediction in Lung Transplant Recipients: The Korean Organ Transplantation Registry
Our machine learning-based model effectively predicts 1-year graft failure in lung transplant recipients using a minimal set of pretransplant variables. Further validation is needed to confirm its clinical applicability.
Authors
- Kyeongman Jeon (ORCID: https://orcid.org/0000-0002-4822-1772)
- Sunyoung Kwon (ORCID: https://orcid.org/0000-0003-3433-1409)
- Dasom Noh (ORCID: https://orcid.org/0009-0008-8021-2295)
- Jin Gu Lee (ORCID: https://orcid.org/0000-0003-2767-6505)
- Samina Park (ORCID: https://orcid.org/0000-0001-9625-2672)
- Song Yee Kim (ORCID: https://orcid.org/0000-0001-8627-486X)
- Hye Ju Yeo (ORCID: https://orcid.org/0000-0002-8403-5790)
- Woo Hyun Cho (ORCID: https://orcid.org/0000-0002-8299-8008)
Institutions
- Yonsei University (KR)
- Severance Hospital (KR)
- Samsung Medical Center (KR)
- Seoul National University Hospital (KR)
- Gangnam Severance Hospital (KR)
- Pusan National University Yangsan Hospital (KR)
- Pusan National University (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Clinical Transplantation
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1111/ctr.70268
- Primary Topic
- Transplantation: Methods and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Science and ICT, South Korea
- National Institutes of Health
- Institute for Information and Communications Technology Promotion