Predicting short-term mortality in severe cirrhosis: An interpretable machine learning model integrating routine clinical indicators
Our prediction model identifies high-risk cirrhotic patients and highlights critical prognostic factors, offering clinicians a valuable tool for risk stratification and timely intervention. The strong correlation between laboratory markers, complications, and outcomes underscores the importance of close monitoring in this population. However, our model is an initial step, effective within the ICU but requiring external, multi-center studies to broaden its clinical applicability, which is a clear priority for our future work.
Authors
- Xudong Wen (ORCID: https://orcid.org/0009-0007-3401-7133)
- Tao Pan
- Rui Liu
- Zhengjie Li
- Shun Zhang
Institutions
- Chengdu University of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2026-03-03
- DOI
- https://doi.org/10.1371/journal.pone.0328952
- Primary Topic
- Liver Disease and Transplantation
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China