Development and Spatial External Validation of a Predictive Model of Survival Based on Random Survival Forest Analysis for People Living With HIV and AIDS After Highly Active Antiretroviral Therapy in China: Retrospective Cohort Study
A machine learning-based RSF model demonstrated promising potential for providing personalized and accurate survival predictions and effective prognostic stratification for people living with HIV and AIDS following HAART in China. Compared to the Cox model, the RSF model exhibited slightly superior performance. A web-based application of the RSF model provides a practical tool for risk assessment and clinical decision-making.
Authors
- Boun Kim Tan (ORCID: https://orcid.org/0000-0003-2617-3113)
- Hongjie Shi (ORCID: https://orcid.org/0000-0001-9236-2460)
- Zhengping Zhu (ORCID: https://orcid.org/0000-0003-3201-299X)
- Xiaoyi Zhou (ORCID: https://orcid.org/0000-0003-0264-6342)
- Xin Li (ORCID: https://orcid.org/0000-0002-3343-8012)
- Yanhui Li (ORCID: https://orcid.org/0000-0001-7838-8277)
- Xiaoshan Li (ORCID: https://orcid.org/0000-0003-1699-2545)
- Ping Zhu (ORCID: https://orcid.org/0000-0002-7264-197X)
- Yuanyuan Xu (ORCID: https://orcid.org/0000-0002-8403-2215)
Institutions
- Chinese Center For Disease Control and Prevention (CN)
- Nantong University (CN)
- Nanjing Municipal Center for Disease Control And Prevention (CN)
- Wuxi People's Hospital (CN)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-05-08
- DOI
- https://doi.org/10.2196/71257
- Citations
- 2
- Primary Topic
- HIV-related health complications and treatments
- Type
- article
- Field-Weighted Citation Impact
- 4.51