Network context matters: graph convolutional network model over social networks improves the detection of unknown HIV infections among young men who have sex with men.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 31197365

Abstract

OBJECTIVE: HIV infection risk can be estimated based on not only individual features but also social network information. However, there have been insufficient studies using n machine learning methods that can maximize the utility of such information. Leveraging a state-of-the-art network topology modeling method, graph convolutional networks (GCN), our main objective was to include network information for the task of detecting previously unknown HIV infections.

Authors

  • Yang Xiang
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
  • Kayo Fujimoto
    Department of Health Promotion & Behavioral Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • John Schneider
    Departments of Medicine and Public Health Sciences, University of Chicago, Chicago, Illinois, USA.
  • Yuxi Jia
    The University of Texas School of Biomedical Informatics, 7000 Fannin St Suite 600, Houston, TX, 77030, USA.
  • Degui Zhi
    School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Cui Tao
    The University of Texas Health Science Center at Houston, USA.

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