Predicting the acute pancreatitis severity with multi-machine learning models: constructing an online prediction platform
Our study highlights the substantial predictive potential of Ca, WBC, α-HBDH, and Glu for SAP. We have built a predictive online platform for clinical use, enabling healthcare professionals to rapidly and effectively assess SAP risk, thereby facilitating timely intervention and treatment.
Authors
- Shike Long (ORCID: https://orcid.org/0000-0001-6771-3361)
- Ribin Liao
- An Chen Fu
- Huan Liu (ORCID: https://orcid.org/0000-0003-1756-1368)
- Yan Liu (ORCID: https://orcid.org/0000-0001-7330-1793)
- Xiyou Li
- Jie Cao
Institutions
- Vaughn College of Aeronautics and Technology (US)
- Guilin Medical University (CN)
- Autonomous Healthcare (US)
- American Institute of Aeronautics and Astronautics (US)
- Nanjing University of Aeronautics and Astronautics (CN)
Publication Details
- Journal
- Frontiers in Cellular and Infection Microbiology
- Published
- 2026-02-27
- DOI
- https://doi.org/10.3389/fcimb.2026.1760036
- Primary Topic
- Pancreatitis Pathology and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangxi Province