Machine learning-based predictive model for acute pancreatitis-associated lung injury: a retrospective analysis
We developed and internally validated a machine learning model to predict APALI, showing strong performance in our study population. To support further research and clinical use, we created an open-access web-based risk calculator. Prospective multicenter validation is needed to confirm generalizability. If successful, the tool may support early risk identification and guide interventions to prevent APALI.
Authors
- Qiaoling Ying
- Jie Yang (ORCID: https://orcid.org/0000-0001-5839-7993)
- Yisen Yang (ORCID: https://orcid.org/0000-0001-8976-1874)
- Hongchang Zhao
- Chuanming Zheng
- Zhenjie Wang (ORCID: https://orcid.org/0000-0003-4916-8632)
- Shurui Wang (ORCID: https://orcid.org/0000-0003-3588-8727)
- Zhaohui Du (ORCID: https://orcid.org/0000-0002-2247-697X)
- Qiang Tang (ORCID: https://orcid.org/0000-0001-5022-2099)
- Hong Ma (ORCID: https://orcid.org/0000-0002-2805-3919)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- First Affiliated Hospital of Bengbu Medical College (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-08-12
- DOI
- https://doi.org/10.3389/fmed.2025.1638097
- Citations
- 1
- Primary Topic
- Pancreatitis Pathology and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.01
Funders
- National Natural Science Foundation of China