Latent class analysis and machine learning for clinical subtyping prediction and differentiation in suspected neurosyphilis patients
This study defines three clinically meaningful latent subtypes of neurosyphilis. The developed XGBoost model effectively discriminates between these subtypes of neurosyphilis and non-neurosyphilis in clinical settings, facilitating timely diagnosis and treatment.
Authors
- Jielun Deng
- Lan Luo (ORCID: https://orcid.org/0000-0001-5027-6003)
- Yike Huang (ORCID: https://orcid.org/0000-0002-8450-2387)
- Fei Ye (ORCID: https://orcid.org/0000-0002-6077-0969)
- Dongdong Li (ORCID: https://orcid.org/0000-0002-0290-6485)
- Sirui Wu (ORCID: https://orcid.org/0000-0002-1057-6121)
- Yuan-Fang Wang (ORCID: https://orcid.org/0009-0004-8390-0561)
Institutions
- Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
Publication Details
- Journal
- Frontiers in Cellular and Infection Microbiology
- Published
- 2025-11-25
- DOI
- https://doi.org/10.3389/fcimb.2025.1665468
- Citations
- 1
- Primary Topic
- Syphilis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.16
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Sichuan Province