A prediction model for postoperative respiratory failure in patients with traumatic cervical spinal cord injury using interpretable machine learning
Interpretable machine learning (IML) models can serve as reliable tools for predicting postoperative RF. The XGBoost model has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention in clinical decision-making.
Authors
- Hui Xing (ORCID: https://orcid.org/0000-0003-2803-7991)
- Minghan Liu (ORCID: https://orcid.org/0000-0002-8572-7668)
- Chao Zhang (ORCID: https://orcid.org/0000-0002-2546-7753)
- Xianjun Ren
- Changqing Li (ORCID: https://orcid.org/0000-0002-0278-0798)
- Miao Yu
- Tao Jiang
Institutions
- Army Medical University (CN)
- Chongqing University (CN)
- Xinqiao Hospital (CN)
- Chongqing Science and Technology Commission (CN)
- Chongqing Municipal Health Commission (CN)
Publication Details
- Journal
- European Spine Journal
- Published
- 2025-12-03
- DOI
- https://doi.org/10.1007/s00586-025-09549-7
- Primary Topic
- Spinal Cord Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Centre Scientifique et Technique du Bâtiment
- National Key Research and Development Program of China