Machine learning-based prognostic prediction for hospitalized HIV/AIDS patients with cryptococcus infection in Guangxi, China
RF and SVM machine learning models have shown promising predictive abilities for the prognosis of hospitalized HIV/AIDS patients with cryptococcosis, which can aid clinical assessment and treatment decisions for patient prognosis.
Authors
- Xiaotao He (ORCID: https://orcid.org/0009-0002-3622-0358)
- Junjun Jiang (ORCID: https://orcid.org/0000-0003-4905-9348)
- Wudi Wei (ORCID: https://orcid.org/0000-0001-6703-4529)
- Baili Zhan (ORCID: https://orcid.org/0009-0007-7639-7824)
- Xiuli Bao
- Hao Liang (ORCID: https://orcid.org/0000-0002-9045-9717)
- Xiaoting Xie (ORCID: https://orcid.org/0000-0002-3779-4786)
- Shixiong Yang (ORCID: https://orcid.org/0000-0002-9929-3151)
- Sirun Meng (ORCID: https://orcid.org/0009-0003-4735-3233)
- Zhiman Xie
- Li Ye
- Meng Zhang (ORCID: https://orcid.org/0009-0004-5279-5164)
Institutions
- Guangxi Medical University (CN)
- The Second Nanning People's Hospital (CN)
- Guangxi Center for Disease Prevention and Control (CN)
- First People's Hospital of Nanning (CN)
- 303 Hospital of People's Liberation Army (CN)
Publication Details
- Journal
- BMC Infectious Diseases
- Published
- 2024-10-08
- DOI
- https://doi.org/10.1186/s12879-024-10013-y
- Citations
- 5
- Primary Topic
- Fungal Infections and Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.93
Funders
- Guangxi Medical University