A machine learning approach for predicting 72-hour mortality of hypothermic patients only using non-invasive parameters: A multi-center retrospective cohort study
This study developed a mortality prediction method for hypothermia patients only using non-invasive parameters, demonstrating robustness and reliability during multi-center validation. It can offer decision support for remote areas and disaster sites where it is difficult to access invasive parameters.
Authors
- Huiquan Wang (ORCID: https://orcid.org/0000-0002-7896-303X)
- Chunliang Jiang (ORCID: https://orcid.org/0000-0003-1531-6289)
- Xin Ma (ORCID: https://orcid.org/0000-0001-7847-911X)
- Guanjun Liu (ORCID: https://orcid.org/0000-0002-7523-4827)
- Guang Zhang (ORCID: https://orcid.org/0000-0002-5233-071X)
- Jing Yuan (ORCID: https://orcid.org/0000-0002-8375-8858)
- YiJing Fu
- G. Ru
- Jiameng Xu (ORCID: https://orcid.org/0000-0003-3695-8221)
Institutions
- Tianjin University of Technology (CN)
- Tiangong University (CN)
- Chinese People's Liberation Army (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-10-22
- DOI
- https://doi.org/10.1371/journal.pone.0334526
- Primary Topic
- Thermal Regulation in Medicine
- Type
- article
- Field-Weighted Citation Impact
- 0.00