Integrating Etiological Insights With Machine Learning for Precision Diagnosis of Obstructive Jaundice: Findings From a High-Volume Center
This study elucidates the etiological spectrum of OJ, meanwhile providing a practical and interpretable ML-based diagnostic tool. By leveraging large-scale clinical data, our model provides a rapid and reliable primary assessment for patients with OJ, enabling clinicians to identify potential etiologies and guide further diagnostic workup.
Authors
- Ningyuan Wen
- Jianrong Xu (ORCID: https://orcid.org/0000-0002-2168-2296)
- Yaoqun Wang (ORCID: https://orcid.org/0000-0003-0122-122X)
- Nansheng Cheng (ORCID: https://orcid.org/0000-0002-3923-6235)
- Xingyu Pu
- Yuan Tian (ORCID: https://orcid.org/0000-0002-6785-0566)
- Geng Liu (ORCID: https://orcid.org/0000-0002-6751-3084)
- Jiong Lu
- Geng Liu
- Xianze Xiong (ORCID: https://orcid.org/0000-0003-0906-6690)
- Shaofeng Wang (ORCID: https://orcid.org/0009-0009-2958-4883)
- Di Zeng
Institutions
- Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
Publication Details
- Journal
- Clinical and Translational Gastroenterology
- Published
- 2025-05-28
- DOI
- https://doi.org/10.14309/ctg.0000000000000849
- Citations
- 1
- Primary Topic
- Gallbladder and Bile Duct Disorders
- Type
- article
- Field-Weighted Citation Impact
- 1.52