Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning
Our study developed a robust predictive model using six clinical features, offering a valuable tool for clinical decision-making and personalized prevention strategies for PPI in patients with diabetes.
Authors
- Bingbing Xiang (ORCID: https://orcid.org/0000-0001-8090-762X)
- Chunxiu Zhao
- Qiaoli Liu (ORCID: https://orcid.org/0000-0001-6043-4909)
- Jie Zhang (ORCID: https://orcid.org/0000-0002-1985-5485)
- Pingliang Yang (ORCID: https://orcid.org/0000-0002-8572-3800)
- Shun Wang (ORCID: https://orcid.org/0009-0002-2601-3275)
Institutions
- Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
- Third People's Hospital of Chengdu (CN)
- First Affiliated Hospital of Chengdu Medical College (CN)
- Southwest Jiaotong University (CN)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2024-12-04
- DOI
- https://doi.org/10.3389/fphys.2024.1501854
- Citations
- 1
- Primary Topic
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Type
- article
- Field-Weighted Citation Impact
- 0.42