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

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

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...

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Bibliographic Details
Main Authors: Werner Maokola, Prosper F Njau, Sandra I McCoy, Huiyu Hu, Laura J Packel, Amon Sabasaba, Zhongming Xie, Jillian L Kadota, Matilda Mlowe, Sylvester Kwilasa, Siraji Shabani, Jingshen Wang
Format: Article
Language:English
Published: BMJ Publishing Group 2024-09-01
Series:BMJ Open
Online Access:https://bmjopen.bmj.com/content/14/9/e088782.full

Internet

https://bmjopen.bmj.com/content/14/9/e088782.full

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