Use of electronic health record data and machine learning to identify candidates for HIV pre-exposure prophylaxis: a modelling study.

ABSTRACT:

Background

The limitations of existing HIV risk prediction tools are a barrier to implementation of pre-exposure prophylaxis (PrEP). We developed and validated an HIV prediction model to identify potential PrEP candidates in a large health-care system.

Methods

Our study population was HIV-uninfected adult members of Kaiser Permanente Northern California, a large integrated health-care system, who were not yet using PrEP and had at least 2 years of previous health plan enrolment with at least one outpatient visit from Jan 1, 2007, to Dec 31, 2017. Using 81 electronic health record (EHR) variables, we applied least absolute shrinkage and selection operator (LASSO) regression to predict incident HIV diagnosis within 3 years on a subset of patients who entered the cohort in 2007

SUBMITTER: Marcus JL 

PROVIDER: S-EPMC7152802 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature