Ensemble clustering of longitudinal bivariate HIV biomarker profiles to group patients by patterns of disease progression.

ABSTRACT: This paper describes an ensemble cluster analysis of bivariate profiles of HIV biomarkers, viral load and CD4 cell counts, which jointly measure disease progression. Data are from a prevalent cohort of HIV positive participants in a clinical trial of vitamin supplementation in Botswana. These individuals were HIV positive upon enrollment, but with unknown times of infection. To categorize groups of participants based on their patterns of progression of HIV infection using both biomarkers, we combine univariate shape-based cluster results for multiple biomarkers through the use of ensemble clustering methods. We first describe univariate clustering for each of the individual biomarker profiles, and make use of shape-respecting distances for clustering the longitudinal profile data. In our d

SUBMITTER: Lynch ML 

PROVIDER: S-EPMC9064718 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature