Using Machine Learning Techniques to Predict Viral Suppression Among People With HIV.

Journal: Journal of acquired immune deficiency syndromes (1999)
PMID: 39561000

Abstract

BACKGROUND: This study aims to develop and examine the performance of machine learning (ML) algorithms in predicting viral suppression among statewide people living with HIV (PWH) in South Carolina.

Authors

  • Xueying Yang
    South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
  • Ruilie Cai
    Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
  • Yunqing Ma
    Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
  • Hao H Zhang
    Department of Mathematics, University of Arizona, Tucson.
  • Xiaowen Sun
    School of Business, Macau University of Science and Technology, Macau 999078, China.
  • Bankole Olatosi
    South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
  • Sharon Weissman
    South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
  • Xiaoming Li
    Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China.
  • Jiajia Zhang
    Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiang Ya School of Public Health, Central South University, Changsha 410078, China.

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