Using a machine learning approach to explore predictors of healthcare visits as missed opportunities for HIV diagnosis.

ABSTRACT:

Objectives

A significant number of individuals with a new HIV diagnosis are still late presenters despite numerous healthcare encounters prior to HIV diagnosis. We employed a machine learning approach to identify the predictors for the missed opportunities for earlier HIV diagnosis.

Methods

The cohort comprised of individuals who were diagnosed with HIV in South Carolina from January 2008 to December 2016. Late presenters (LPs) (initial CD4 ≤200 cells/mm3 within one month of HIV diagnosis) with any healthcare visit during three years prior to HIV diagnosis were defined as patients with a missed opportunity. Using least absolute shrinkage and selection operator (LASSO) regression, two prediction models were developed to capture the impact of facility type (model 1) and physic

SUBMITTER: Weissman S 

PROVIDER: S-EPMC8172090 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature