Machine learning to improve HIV screening using routine data in Kenya.

Journal: Journal of the International AIDS Society
PMID: 40254897

Abstract

INTRODUCTION: Optimal use of HIV testing resources accelerates progress towards ending HIV as a global threat. In Kenya, current testing practices yield a 2.8% positivity rate for new diagnoses reported through the national HIV electronic medical record (EMR) system. Increasingly, researchers have explored the potential for machine learning to improve the identification of people with undiagnosed HIV for referral for HIV testing. However, few studies have used routinely collected programme data as the basis for implementing a real-time clinical decision support system to improve HIV screening. In this study, we applied machine learning to routine programme data from Kenya's EMR to predict the probability that an individual seeking care is undiagnosed HIV positive and should be prioritized for testing.

Authors

  • Jonathan D Friedman
    Data Science, Palladium Group, Washington, DC, USA.
  • Jonathan M Mwangi
    Division of Global, HIV & TB, US Centers for Disease Control and Prevention, Nairobi, Kenya.
  • Kennedy J Muthoka
    Kenya Health Information Management Systems (KeHMIS) III Project, Palladium Kenya, Nairobi, Kenya.
  • Benedette A Otieno
    Kenya Health Information Management Systems (KeHMIS) III Project, Palladium Kenya, Nairobi, Kenya.
  • Jacob O Odhiambo
    Kenya Health Information Management Systems (KeHMIS) III Project, Palladium Kenya, Nairobi, Kenya.
  • Frederick O Miruka
    Division of Global, HIV & TB, US Centers for Disease Control and Prevention, Nairobi, Kenya.
  • Lilly M Nyagah
    National AIDS and STIs Control Programme, Ministry of Health, Nairobi, Kenya.
  • Pascal M Mwele
    Kenya Health Information Management Systems (KeHMIS) III Project, Palladium Kenya, Nairobi, Kenya.
  • Edmon O Obat
    USAID United States Agency for International Development, Health Population and Nutrition Office, Nairobi, Kenya.
  • Gonza O Omoro
    U.S. Department of Defense, Nairobi, Kenya.
  • Margaret M Ndisha
    Division of Global, HIV & TB, US Centers for Disease Control and Prevention, Nairobi, Kenya.
  • Davies O Kimanga
    Division of Global, HIV & TB, US Centers for Disease Control and Prevention, Nairobi, Kenya.

Keywords

External Resources

Popular Topics
Recent Journals