Interpretable machine learning model for predicting covert hepatic encephalopathy in patients with cirrhosis: a multicenter study
This study presents a novel ML-based approach for predicting CHE in cirrhotic patients, with LightGBM offering the best balance of performance and interpretability. The identified clinical and demographic predictors could facilitate early CHE detection and personalized management, ultimately improving outcomes for this high-risk population.
Authors
- Peiqin Wang (ORCID: https://orcid.org/0009-0009-7345-9510)
- Yilong Liu (ORCID: https://orcid.org/0000-0002-0055-8333)
- Chuan Yin (ORCID: https://orcid.org/0000-0002-5303-8278)
- Yiwen Qiu (ORCID: https://orcid.org/0000-0003-0022-4824)
- Xin Zeng (ORCID: https://orcid.org/0000-0002-4180-3366)
- Ruoyao Wang
- Kai Ding (ORCID: https://orcid.org/0000-0002-3508-465X)
Institutions
- Shanghai Changzheng Hospital (CN)
- Shanghai East Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-11-25
- DOI
- https://doi.org/10.3389/fmed.2025.1686005
- Primary Topic
- Liver Disease and Transplantation
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Shanghai Municipal Health Commission
- National Natural Science Foundation of China