Utilizing electronic health record data to understand comorbidity burden among people living with HIV: a machine learning approach.

ABSTRACT:

Objectives

An understanding of the predictors of comorbidity among people living with HIV (PLWH) is critical for effective HIV care management. In this study, we identified predictors of comorbidity burden among PLWH based on machine learning models with electronic health record (EHR) data.

Methods

The study population are individuals with a HIV diagnosis between January 2005 and December 2016 in South Carolina (SC). The change of comorbidity burden, represented by the Charlson Comorbidity Index (CCI) score, was measured by the score difference between pre- and post-HIV diagnosis, and dichotomized into a binary outcome variable. Thirty-five risk predictors from multiple domains were used to predict the increase in comorbidity burden based on the logistic least absolute shrin

SUBMITTER: Yang X 

PROVIDER: S-EPMC8058944 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature