Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics
We developed and validated a machine learning-based nomogram to predict mortality in critically ill IBD patients, integrating interpretable predictors from multicenter ICU data.
Authors
- Nian Fang (ORCID: https://orcid.org/0000-0002-2851-6530)
- Zeyu Zhang (ORCID: https://orcid.org/0000-0002-8703-4195)
- Ziling Fang (ORCID: https://orcid.org/0000-0002-3674-156X)
- Peng Liu (ORCID: https://orcid.org/0000-0002-2653-0718)
- Chao Zhao (ORCID: https://orcid.org/0000-0003-1739-1151)
- Ling Yao (ORCID: https://orcid.org/0009-0005-5898-6807)
- Yijun Zhou (ORCID: https://orcid.org/0000-0002-5238-9666)
Institutions
- Jiangxi University of Traditional Chinese Medicine (CN)
- Nanchang University (CN)
- Second Affiliated Hospital of Nanchang University (CN)
- First Affiliated Hospital of Nanchang University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-08-08
- DOI
- https://doi.org/10.3389/fmed.2025.1624899
- Citations
- 1
- Primary Topic
- Inflammatory Bowel Disease
- Type
- article
- Field-Weighted Citation Impact
- 1.82
Funders
- Natural Science Foundation of Jiangxi Province