Interpretable Machine Learning Model Using Oxygenation and Respiratory Variability to Predict Hemorrhagic Stroke Mortality: A Multicenter Validation Study
- DOI
- 10.1016/j.cmpb.2026.109572
- Published
- 2026-11
- Container
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.cmpb.2026.109572,
title = {Interpretable Machine Learning Model Using Oxygenation and Respiratory Variability to Predict Hemorrhagic Stroke Mortality: A Multicenter Validation Study},
author = {Jing Feng and Hongyu Zhang and Jianheng Gu and Chang He and Mengsi Zhan and Hongbo San and Baojian Wei},
year = {2026},
journal = {Computer Methods and Programs in Biomedicine},
doi = {10.1016/j.cmpb.2026.109572},
url = {https://doi.org/10.1016/j.cmpb.2026.109572}
}RIS
TY - JOUR TI - Interpretable Machine Learning Model Using Oxygenation and Respiratory Variability to Predict Hemorrhagic Stroke Mortality: A Multicenter Validation Study AU - Jing Feng AU - Hongyu Zhang AU - Jianheng Gu AU - Chang He AU - Mengsi Zhan AU - Hongbo San AU - Baojian Wei PY - 2026 JO - Computer Methods and Programs in Biomedicine DO - 10.1016/j.cmpb.2026.109572 UR - https://doi.org/10.1016/j.cmpb.2026.109572 ER -
APA
Feng, J., Zhang, H., Gu, J., He, C., Zhan, M., San, H., & Wei, B. (2026). Interpretable Machine Learning Model Using Oxygenation and Respiratory Variability to Predict Hemorrhagic Stroke Mortality: A Multicenter Validation Study. Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2026.109572
Source records
- crossref · retrieved 2026-09-26T09:36:15.640Z