Predictive machine learning model in intensive care unit patients with acute-on-chronic liver failure and two or more organ failures
We presented explainable, well-validated, and calibrated predictive models for ACLF patients with two or more OFs, which outperformed existing predictive scores.
Authors
- Xiaodan Sun (ORCID: https://orcid.org/0000-0002-5119-383X)
- Hirsh D. Trivedi (ORCID: https://orcid.org/0000-0002-8328-9211)
- Yee Hui Yeo (ORCID: https://orcid.org/0000-0002-2703-5954)
- Soo Young Hwang (ORCID: https://orcid.org/0000-0001-9845-7073)
- Jonel Trebicka (ORCID: https://orcid.org/0000-0002-7028-3881)
- M. McCoy
- Zhujun Cao (ORCID: https://orcid.org/0000-0002-8684-2107)
- Fanpu Ji (ORCID: https://orcid.org/0000-0002-1463-8035)
- Vinay Sundaram (ORCID: https://orcid.org/0000-0002-1450-7756)
- Jian Zu
- Mingjie Zhang (ORCID: https://orcid.org/0000-0001-9404-0190)
- Juan Li (ORCID: https://orcid.org/0000-0002-2140-1845)
- Yuan Wang (ORCID: https://orcid.org/0000-0001-9350-4061)
- Yingli He
- Chun-Ying Wu (ORCID: https://orcid.org/0000-0003-4207-2312)
- Taotao Yan (ORCID: https://orcid.org/0000-0002-3153-4959)
- Yi Liu (ORCID: https://orcid.org/0009-0002-9054-4839)
Institutions
- Cedars-Sinai Medical Center (US)
- National Yang Ming Chiao Tung University (TW)
- Shanghai Jiao Tong University (CN)
- China Medical University (TW)
- University of Münster (DE)
- Odense University Hospital (DK)
- Ruijin Hospital (CN)
- Taipei Veterans General Hospital (TW)
- European Foundation for the Study of Chronic Liver Failure (ES)
- First Affiliated Hospital of Xi'an Jiaotong University (CN)
- Second Affiliated Hospital of Xi'an Jiaotong University (CN)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- Clinical and Molecular Hepatology
- Published
- 2025-09-02
- DOI
- https://doi.org/10.3350/cmh.2025.0573
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.94
Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities