Machine learning model for early prediction of acute respiratory failure in acute pancreatitis: Multicenter validation
Yu Wang, Feng Lv, MingYang Tao, JiXuan Cui, Dan Zhang, Haodong Zhao, Jun Li, GengYun Sun, XingYu Wang · 2026
Read Machine learning model for early prediction of acute respiratory failure in acute pancreatitis: Multicenter validation by Yu Wang, Feng Lv, MingYang Tao, JiXuan Cui, Dan Zhang, Haodong Zhao, Jun Li, GengYun Sun, XingYu Wang (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13311134.txt.
Description
Yu Wang and colleagues build a learning model to foretell breathing failure in acute pancreatitis in a 2026 study. Lung failure is the most common organ failure in this pancreas illness and carries high death risk. Early warning would speed intensive care, so records from a large United States database and Chinese hospitals were mined.
Nine top traits, including temperature, oxygen level, and seven blood measures, fed five learning methods. A logistic model scored highest across home, outside, and cross specialty tests, with strong sensitivity marks. A free web tool hosts the model for clinic use. Tests in pregnant patients marked a boundary of safe use.
Catalog record
| CREATOR | Yu Wang, Feng Lv, MingYang Tao, JiXuan Cui, Dan Zhang, Haodong Zhao, Jun Li, GengYun Sun, XingYu Wang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 52953 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |