Algorithmic prediction of HIV status using nation-wide electronic registry data.

ABSTRACT:

Background

Late HIV diagnosis is detrimental both to the individual and to society. Strategies to improve early diagnosis of HIV must be a key health care priority. We examined whether nation-wide electronic registry data could be used to predict HIV status using machine learning algorithms.

Methods

We extracted individual level data from Danish registries and used algorithms to predict HIV status. We used various algorithms to train prediction models and validated these models. We calibrated the models to mimic different clinical scenarios and created confusion matrices based on the calibrated models.

Findings

A total 4,384,178 individuals, including 4,350 with incident HIV, were included in the analyses. The full model that included all variables that included demog

SUBMITTER: Ahlstrom MG 

PROVIDER: S-EPMC6933258 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature