A KNN-based model for non-invasive prediction of hemorrhagic shock severity in prehospital settings: integrating MAP, PBUCO2, PTCO2, and PPV
Using the detection indicators MAP, P<sub>BU</sub>CO<sub>2</sub>, P<sub>TC</sub>O<sub>2</sub> and PPV, the KNN-based rat hemorrhagic shock severity prediction model has high accuracy and stability, and demonstrates potential feasibility for severity stratification of hemorrhagic shock in standardized preclinical models, providing a foundation for future clinical validation in prehospital environments.
Authors
- Wencai Pan (ORCID: https://orcid.org/0000-0001-9551-7464)
- Peng Zhao (ORCID: https://orcid.org/0000-0002-9871-8808)
- Yufei Liu (ORCID: https://orcid.org/0000-0001-8645-2988)
- Yang Li (ORCID: https://orcid.org/0000-0001-5260-5157)
- Shi Hui Zhang (ORCID: https://orcid.org/0000-0002-0149-0131)
- Xinhua Zou (ORCID: https://orcid.org/0000-0001-7113-8232)
- Jiaqing Yang
Institutions
- Army Medical University (CN)
- Chongqing University (CN)
- Daping Hospital (CN)
- Xinqiao Hospital (CN)
- Ministry of Education (TH)
- Chongqing University of Technology (CN)
Publication Details
- Journal
- BioMedical Engineering OnLine
- Published
- 2025-05-20
- DOI
- https://doi.org/10.1186/s12938-025-01394-5
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China