Machine learning based clinical prediction model for 1-year mortality in Sepsis patients with atrial fibrillation
AF emerges as a substantial peril for adverse outcomes in sepsis patients. The risk model, encompassing pertinent clinical variables, outperformed existing clinical critical illness scores in mortality prediction. This model furnishes valuable insights for risk stratification, augmenting prognostic precision in sepsis patients with concomitant AF.
Authors
- Hong Meng (ORCID: https://orcid.org/0000-0002-3746-0997)
- He Huang (ORCID: https://orcid.org/0000-0003-3915-2733)
- Bin Kong
- Liang Guo (ORCID: https://orcid.org/0000-0001-5338-4958)
- Wei Shuai (ORCID: https://orcid.org/0000-0002-5507-7369)
- Yucheng Pan (ORCID: https://orcid.org/0000-0002-5986-7149)
Institutions
- Wuhan University (CN)
- Renmin Hospital of Wuhan University (CN)
Publication Details
- Journal
- Heliyon
- Published
- 2024-10-09
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e38730
- Citations
- 4
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- National Natural Science Foundation of China