Interpretable prediction of 30-day mortality in patients with acute pancreatitis based on machine learning and SHAP
the XGBoost model effectively predicts the severity of SAP, serving as a valuable tool for clinicians to identify SAP early.
Authors
- Shuangmei Li
- Yingjie Tian (ORCID: https://orcid.org/0000-0002-4675-0398)
- Tong Wang (ORCID: https://orcid.org/0000-0003-4644-4179)
- Xiaojing Li (ORCID: https://orcid.org/0000-0003-3246-1277)
- Haidong Wu
Institutions
- Sun Yat-sen University (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
- Eighth Affiliated Hospital of Sun Yat-sen University
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-11-05
- DOI
- https://doi.org/10.1186/s12911-024-02741-7
- Citations
- 14
- Primary Topic
- Pancreatitis Pathology and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 8.02
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province
- Shenzhen Fundamental Research Program
- Fundamental Research Funds for the Central Universities