Latent class analysis and machine learning for clinical subtyping prediction and differentiation in suspected neurosyphilis patients.

ABSTRACT:

Objective

Neurosyphilis presents significant diagnostic and therapeutic challenges due to its heterogeneous clinical manifestations, absence of a gold-standard diagnostic criterion, and variable treatment responses. This study aims to identify clinically homogeneous subtypes of suspected neurosyphilis patients and develop a machine learning-based subtyping model to support clinical decision-making.

Methods

Data from 451 suspected neurosyphilis patients were retrospectively collected from West China Hospital of Sichuan University. Patients were divided into a model development cohort (n=369) and an external validation cohort (n=82) by time. Latent class analysis (LCA) was performed to identify subtypes, with the optimal class number determined by model fit indicators. Key pre

SUBMITTER: Wu S 

PROVIDER: S-EPMC12685824 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature