Machine Learning for Predicting Primary Graft Dysfunction After Lung Transplantation: An Interpretable Model Study
The RF model had the optimal performance in PGD3 prediction and postoperative risk stratification for patients after Lung Tx.
Authors
- Hongyang Xu (ORCID: https://orcid.org/0009-0003-8847-3506)
- Wenjun Mao (ORCID: https://orcid.org/0000-0002-4644-9749)
- Wei Xia (ORCID: https://orcid.org/0000-0002-1490-6060)
- Chenghu Song (ORCID: https://orcid.org/0000-0002-2620-5376)
- Jingyu Chen (ORCID: https://orcid.org/0000-0002-2127-1788)
- Xiaokun Wang (ORCID: https://orcid.org/0009-0003-8676-9450)
- He Zhao (ORCID: https://orcid.org/0000-0002-3988-2851)
- Jiwei Liu (ORCID: https://orcid.org/0000-0001-5040-6242)
- Ruo Chen (ORCID: https://orcid.org/0000-0003-2578-8562)
- Weici Liu (ORCID: https://orcid.org/0009-0006-8226-9143)
Institutions
- Wuxi People's Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Transplantation
- Published
- 2025-01-10
- DOI
- https://doi.org/10.1097/tp.0000000000005326
- Citations
- 3
- Primary Topic
- Transplantation: Methods and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 6.02