Detecting pancreaticobiliary maljunction in pediatric congenital choledochal malformation patients using machine learning methods
By integrating preoperative clinical symptoms and laboratory parameters, machine learning algorithms demonstrated significant detection capability in identifying PBM among pediatric congenital choledochal malformation patients, with the RF model achieving superior performance metrics among all base models. The developed ensemble voting classifier provides valuable preoperative guidance for surgical planning and clinical management, enabling detection of PBM comorbidity before surgery in congenital choledochal malformation cases.
Authors
- R Zhang
- Chengji Dong (ORCID: https://orcid.org/0000-0001-7359-6501)
- Hong Yang (ORCID: https://orcid.org/0000-0002-3076-3215)
- Long Li (ORCID: https://orcid.org/0000-0003-0358-3929)
- Yifeng Shao (ORCID: https://orcid.org/0000-0002-6337-1090)
- Mei Diao
- Kunpeng Yang (ORCID: https://orcid.org/0009-0007-2473-6679)
- Chuanyu Yang
- Chuhan Jiang (ORCID: https://orcid.org/0000-0003-4862-2151)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Medizinische Hochschule Hannover (DE)
- Vall d'Hebron Institut de Recerca (ES)
- Capital Institute of Pediatrics (CN)
- CIBBIM-Nanomedicine (ES)
- Beijing Tsinghua Chang Gung Hospital (CN)
- China United Network Communications Group (China) (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- BMC Surgery
- Published
- 2025-10-03
- DOI
- https://doi.org/10.1186/s12893-025-03154-7
- Primary Topic
- Pediatric Hepatobiliary Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chinese Academy of Medical Sciences