Prevention of adverse HIV treatment outcomes: machine learning to enable proactive support of people at risk of HIV care disengagement in Tanzania.

ABSTRACT:

Objectives

This study aimed to develop a machine learning (ML) model to predict disengagement from HIV care, high viral load or death among people living with HIV (PLHIV) with the goal of enabling proactive support interventions in Tanzania. The algorithm addressed common challenges when applying ML to electronic medical record (EMR) data: (1) imbalanced outcome distribution; (2) heterogeneity across multisite EMR data and (3) evolving virological suppression thresholds.

Design

Observational study using a national EMR database.

Setting

Conducted in two regions in Tanzania, using data from the National HIV Care database.

Participants

The study included over 6 million HIV care visit records from 295 961 PLHIV in two regions in Tanzania's National HIV Care databas

SUBMITTER: Xie Z 

PROVIDER: S-EPMC11423721 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature