Interpretable machine learning models for predicting in-hospital mortality in patients with chronic critical illness and heart failure: A multicenter study
The SVM model developed reliably predicts in-hospital mortality in patients with CCI and HF, offering a valuable tool for early intervention and enhanced patient management.
Authors
- Xiaocong Zeng (ORCID: https://orcid.org/0000-0002-6430-7736)
- Liangbo Hu
- Pengzhan Li
- Guoqing Liu (ORCID: https://orcid.org/0000-0002-2221-3651)
- Yongqi Lin (ORCID: https://orcid.org/0009-0001-3985-2388)
- Min He (ORCID: https://orcid.org/0009-0006-2357-6266)
- Siyu Ren (ORCID: https://orcid.org/0009-0006-9958-713X)
- Lingyan Lei (ORCID: https://orcid.org/0009-0001-8930-8583)
- Qianling Zhang (ORCID: https://orcid.org/0000-0001-6942-3958)
- Yue Wang (ORCID: https://orcid.org/0000-0003-4960-2208)
- Xueshuang Bei
Institutions
- Guangxi Medical University (CN)
- First Affiliated Hospital of GuangXi Medical University (CN)
- Guangxi Center for Disease Prevention and Control (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251347785
- Citations
- 7
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 17.37
Funders
- National Natural Science Foundation of China