Machine Learning Model for Predicting Biliary Complications After Liver Transplantation
The ML algorithm was able to identify risk factors of BCs in all postoperative periods and this supplied insights in patient management.
Authors
- Renhua Ju
- Leida Zhang (ORCID: https://orcid.org/0000-0002-3870-7410)
- Xingchao Liu
- Jun Li (ORCID: https://orcid.org/0000-0002-8664-4813)
- Chengcheng Zhang (ORCID: https://orcid.org/0000-0002-0550-107X)
- Guangyi Liu (ORCID: https://orcid.org/0000-0001-6053-1987)
- Hongfei Zeng
- Di Jiang (ORCID: https://orcid.org/0000-0003-2309-1809)
- Yuancheng Li (ORCID: https://orcid.org/0000-0003-1994-6093)
- Feng Hu (ORCID: https://orcid.org/0009-0007-2419-585X)
Institutions
- Army Medical University (CN)
- Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (CN)
- Southwest Hospital (CN)
- Liaocheng People's Hospital (CN)
- Chongqing Medical and Pharmaceutical College (CN)
Publication Details
- Journal
- Clinical and Translational Gastroenterology
- Published
- 2025-04-18
- DOI
- https://doi.org/10.14309/ctg.0000000000000843
- Citations
- 3
- Primary Topic
- Organ Transplantation Techniques and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 6.02