Machine Learning Identifies Sexual Behavior Subgroups Among Men Who Have Sex with Men in Switzerland.

ABSTRACT: Sexual behavior is heterogeneous and dynamic. Characterization of such complexity constitutes evidence for public health authorities and caregivers concerned with the framing of sexual health messages aimed at specific subgroups. We developed a machine-learning-based methodology for inference and characterization of such subgroups from longitudinal data on men who have sex with men (MSM) attending individual sexual health counseling sessions. Because longitudinal data take time to record, we assessed the ability of first visit data to predict subgroups' membership. Our methodology comprised two main steps: (1) Hierarchical clustering to group 2349 HIV-negative MSM based on their self-reported longitudinal sexual behavior during visits to Swiss sexual health counseling centers between Novem

SUBMITTER: Salazar-Vizcaya L 

PROVIDER: S-EPMC12283876 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature