Explainable artificial intelligence and domain adaptation for predicting HIV infection with graph neural networks.

Journal: Annals of medicine
PMID: 39417227

Abstract

OBJECTIVE: Investigation of explainable deep learning methods for graph neural networks to predict HIV infections with social network information and performing domain adaptation to evaluate model transferability across different datasets.

Authors

  • Evan Yu
    School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.
  • Jingcheng Du
    University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • Yang Xiang
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
  • Xinyue Hu
    Beijing Eaglevision Technology Development, Beijing, China.
  • Jingna Feng
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, United States.
  • Xi Luo
    Department of Stomatology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • John A Schneider
    Departments of Medicine and Public Health Sciences, University of Chicago, Chicago, IL, USA.
  • Degui Zhi
    School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Kayo Fujimoto
    Department of Health Promotion & Behavioral Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • Cui Tao
    The University of Texas Health Science Center at Houston, USA.

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