Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit
ML models have shown promising reliability in predicting in-hospital mortality among patients with AP in the ICU. Among these models, the CatBoost model exhibits superior predictive performance, providing valuable assistance to clinical practitioners in identifying high-risk patients and facilitating early interventions to enhance prognosis. The development of a compact model and a web-based calculator further enhances the convenience of using these models in clinical practice.
Authors
- Xiya Wang (ORCID: https://orcid.org/0000-0002-1126-5595)
- Hongmeng Dong
- Yongsheng Zhang (ORCID: https://orcid.org/0009-0009-0714-3374)
- Ying Chen (ORCID: https://orcid.org/0000-0001-7876-4877)
- Weidong Yao (ORCID: https://orcid.org/0000-0003-0429-4053)
- Shuxing Wei (ORCID: https://orcid.org/0000-0002-5179-7473)
- W. Ji (ORCID: https://orcid.org/0009-0004-5704-4431)
- Shubin Guo (ORCID: https://orcid.org/0009-0009-9783-7782)
Institutions
- Shandong University of Traditional Chinese Medicine (CN)
- Beijing Chao-Yang Hospital, Capital Medical University (CN)
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-05-27
- DOI
- https://doi.org/10.1186/s12911-025-03033-4
- Citations
- 5
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 12.40
Funders
- Beijing Municipal Science and Technology Commission