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.

Journal: BMC medical informatics and decision making
Published Date: May 19, 2025

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 and proactive interventions. This study aimed to develop a prediction model to assess the future risk of LTFU in HIV care in Ethiopia.

Authors

  • Tamrat Endebu
    Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia. seetame2008@gmail.com.
  • Girma Taye
    Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia.
  • Wakgari Deressa
    Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia.

Keywords

External Resources

Popular Topics
Recent Journals