Explainable machine learning for the assessment of donor grafts in liver transplantation
The developed model for predicting postoperative DGF demonstrated excellent predictive performance, aiding clinicians in evaluating donor grafts and making informed decisions.
Authors
- Hua Li (ORCID: https://orcid.org/0000-0001-9644-6768)
- Zhang Ming-shen
- Xiao Xu (ORCID: https://orcid.org/0000-0002-2761-2811)
- Guihua Chen (ORCID: https://orcid.org/0000-0001-8134-0194)
- Shuhong Yi (ORCID: https://orcid.org/0000-0003-1837-7837)
- Zhixing Liang
- Peng Jiang (ORCID: https://orcid.org/0000-0002-6057-7624)
- Linsen Ye (ORCID: https://orcid.org/0000-0001-9632-1949)
- Siqi Li (ORCID: https://orcid.org/0000-0002-4569-1111)
- Kun Li (ORCID: https://orcid.org/0000-0002-0678-1122)
- Wei Liu (ORCID: https://orcid.org/0000-0003-0012-6845)
- Siyi Dong
- Shusen Zheng
- Yang Yang
- Yu Haoyuan
- Hu Yongwei
Institutions
- Sun Yat-sen University (CN)
- Blood Center of Zhejiang Province (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
- Hangzhou Red Cross Hospital (CN)
- Hangzhou Medical College (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Hepatology Research
- Published
- 2025-04-04
- DOI
- https://doi.org/10.1111/hepr.14187
- Primary Topic
- Organ Transplantation Techniques and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Sun Yat-sen University
- Ministry of Science and Technology of the People's Republic of China
- Natural Science Foundation of Guangdong Province
- Science and Technology Planning Project of Guangdong Province