Predicting survival rates of critically ill septic patients with heart failure using interpretable machine learning models
Our DeepSurv model was the most comprehensive interpretable prediction model specifically developed and validated for septic critically ill patients with HF. It demonstrated good model performance in predicting the 28-day survival rate of such patients and will provide valuable decision support for clinicians.
Authors
- Xin Zhang (ORCID: https://orcid.org/0000-0003-2062-4868)
- Gong‐Hao He (ORCID: https://orcid.org/0000-0002-1274-3130)
- Li-Juan Yang
- Sun‐Jun Yin
- De-Min Li (ORCID: https://orcid.org/0009-0001-9404-1334)
- Fang Yu
- Yu Guo
- Haiying Yang (ORCID: https://orcid.org/0000-0002-8852-8672)
- Jia-De Zhu
- Meng-Han Jiang (ORCID: https://orcid.org/0009-0003-7009-8736)
Institutions
- Dali University (CN)
Publication Details
- Journal
- Technology and Health Care
- Published
- 2025-06-11
- DOI
- https://doi.org/10.1177/09287329251346284
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.48