Predictive survival modelings for HIV-related cryptococcosis: comparing machine learning approaches
This study presents comprehensive and multidimensional approaches to overcome the challenges commonly encountered in real-world clinical settings. By applying cytokine-based clustering, we illustrate the potential for more nuanced severity stratification, offering a fresh perspective on disease progression. In parallel, our penalized survival model provides a step forward in personalized risk assessment, supporting informed clinical decisions and customized patient management. These findings suggest promising directions for individualized healthcare solutions, leveraging machine learning to enhance survival predictions in HIV-related cryptococcosis.
Authors
- Xuemin Fu
- Hongzhou Lu (ORCID: https://orcid.org/0000-0003-3590-4790)
- Zhihang Zheng
- Luling Wu (ORCID: https://orcid.org/0000-0001-6574-5861)
- Benno Pütz (ORCID: https://orcid.org/0000-0002-2208-209X)
- Bertram Müller‐Myhsok (ORCID: https://orcid.org/0000-0002-0719-101X)
- Yinzhong Shen (ORCID: https://orcid.org/0000-0002-7604-5139)
- Jingna Xun (ORCID: https://orcid.org/0000-0002-6086-6960)
- Jun Chen (ORCID: https://orcid.org/0000-0002-3850-4875)
- Yanpeng Li (ORCID: https://orcid.org/0000-0002-0643-4809)
Institutions
- Southern University of Science and Technology (CN)
- Shanghai Public Health Clinical Center (CN)
- Shanghai Pulmonary Hospital (CN)
- Max Planck Institute of Psychiatry (DE)
- Shenzhen Third People’s Hospital (CN)
Publication Details
- Journal
- Frontiers in Cellular and Infection Microbiology
- Published
- 2025-05-02
- DOI
- https://doi.org/10.3389/fcimb.2025.1542707
- Citations
- 1
- Primary Topic
- Fungal Infections and Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- NIH Clinical Center