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

This study developed a machine learning-based predictive model for assessing the future risk of LTFU in HIV care within low-resource settings. Notably, the model built via the random forest algorithm exhibited high accuracy and strong discriminative performance, highlighting its positive net benefit for clinical applications. Furthermore, ongoing external validation across diverse populations is important to ensure the model's reliability and generalizability.