Breaking new ground: machine learning enhances survival forecasts in hypercapnic respiratory failure
The RSF model offered distinct advantages over the CoxPH and DeepSurv models in terms of clinical evaluation and monitoring of patients with hypercapnic respiratory failure.
Authors
- H. Hu (ORCID: https://orcid.org/0000-0001-7748-1517)
- Zhixiao Sun (ORCID: https://orcid.org/0000-0002-7364-235X)
- Zhongxiang Liu
- Shuanying Yang (ORCID: https://orcid.org/0000-0002-9676-9302)
- Yuan Yin (ORCID: https://orcid.org/0000-0001-9654-7374)
- Jianyang Lin (ORCID: https://orcid.org/0000-0002-6273-2704)
- Bingqing Zuo
Institutions
- Xuzhou Medical College (CN)
- Nantong University (CN)
- Yancheng First People's Hospital (CN)
- Second Affiliated Hospital of Xi'an Jiaotong University (CN)
- Jiangsu Province Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-02-20
- DOI
- https://doi.org/10.3389/fmed.2025.1497651
- Citations
- 3
- Primary Topic
- Respiratory Support and Mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 4.56