Development of a Machine Learning Modeling Tool for Predicting HIV Incidence Using Public Health Data From a County in the Southern United States.

Journal: Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
PMID: 38393832

Abstract

BACKGROUND: Advancements in machine learning (ML) have improved the accuracy of models that predict human immunodeficiency virus (HIV) incidence. These models have used electronic medical records and registries. We aim to broaden the application of these tools by using deidentified public health datasets for notifiable sexually transmitted infections (STIs) from a southern US county known for high HIV incidence. The goal is to assess the feasibility and accuracy of ML in predicting HIV incidence, which could inform and enhance public health interventions.

Authors

  • Carlos S Saldana
    Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.
  • Elizabeth Burkhardt
    Epidemiology Division, Georgia Department of Public Health, Atlanta, Georgia, USA.
  • Alfred Pennisi
    Epidemiology Division, Georgia Department of Public Health, Atlanta, Georgia, USA.
  • Kirsten Oliver
    Epidemiology Division, Georgia Department of Public Health, Atlanta, Georgia, USA.
  • John Olmstead
    Epidemiology Division, Georgia Department of Public Health, Atlanta, Georgia, USA.
  • David P Holland
    Division of Primary Care, Mercy Care Health Systems, Atlanta, Georgia, USA.
  • Jenna Gettings
    Epidemiology Division, Georgia Department of Public Health, Atlanta, Georgia, USA.
  • Daniel Mauck
    Epidemiology Division, Georgia Department of Public Health, Atlanta, Georgia, USA.
  • David Austin
    Epidemiology Division, Georgia Department of Public Health, Atlanta, Georgia, USA.
  • Pascale Wortley
    Epidemiology Division, Georgia Department of Public Health, Atlanta, Georgia, USA.
  • Karla V Saldana Ochoa
    School of Architecture, College of Design, Construction, and Planning, University of Florida, Gainesville, Florida, USA.

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