The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis.

Journal: BMC infectious diseases
Published Date: May 6, 2024

Abstract

BACKGROUND: Early prediction of mortality in individuals with HIV (PWH) has perpetually posed a formidable challenge. With the widespread integration of machine learning into clinical practice, some researchers endeavor to formulate models predicting the mortality risk for PWH. Nevertheless, the diverse timeframes of mortality among PWH and the potential multitude of modeling variables have cast doubt on the efficacy of the current predictive model for HIV-related deaths. To address this, we undertook a systematic review and meta-analysis, aiming to comprehensively assess the utilization of machine learning in the early prediction of HIV-related deaths and furnish evidence-based support for the advancement of artificial intelligence in this domain.

Authors

  • Yuefei Li
    Public Health, Xinjiang Medical University, Urumqi, Xinjiang, 830011, China.
  • Ying Feng
    Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
  • Qian He
    National Translational Science Center for Molecular Medicine and Department of Cell Biology, Fourth Military Medical University, Xi'an, 710032, China.
  • Zhen Ni
    Division of Neurology, Krembil Neuroscience Centre and Toronto Western Research Institute, University Health Network, University of Toronto Toronto, ON, Canada.
  • Xiaoyuan Hu
    STD/HIV Prevention and Control Center, Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention, No. 138 Jianquan 1st Street, Tianshan District, Urumqi, Xinjiang, 830002, China.
  • Xinhuan Feng
    Clinical Laboratory, Second People's Hospital of Yining, Yining, Xinjiang, 835000, China.
  • Mingjian Ni
    STD/HIV Prevention and Control Center, Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention, No. 138 Jianquan 1st Street, Tianshan District, Urumqi, Xinjiang, 830002, China. xjnmj@126.com.

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