Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting

Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting

Abstract Background Despite the global commitment to ending AIDS by 2030, the loss of follow-up (LTFU) in HIV care remains a significant challenge. To address this issue, a data-driven clinical decision tool is crucial for identifying patients at greater risk of LTFU and facilitating personalized an...

Full description

Saved in:
Bibliographic Details
Main Authors: Tamrat Endebu, Girma Taye, Wakgari Deressa
Format: Article
Language:English
Published: BMC 2025-05-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Machine learning
Model development
Prediction tool
Loss to follow-up
HIV
Low-resource setting
Online Access:https://doi.org/10.1186/s12911-025-03030-7
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s12911-025-03030-7

Similar Items