Explainable artificial intelligence and domain adaptation for predicting HIV infection with graph neural networks
GAT models can be used to address the data sparsity issue in HIV study populations. They are powerful tools for predicting individual risk of HIV that can be further explored for better understanding of HIV transmission.
Authors
- John A. Schneider (ORCID: https://orcid.org/0000-0002-7870-5639)
- Degui Zhi (ORCID: https://orcid.org/0000-0001-7754-1890)
- Xi Luo (ORCID: https://orcid.org/0000-0002-0909-9372)
- Jingna Feng (ORCID: https://orcid.org/0000-0002-0434-2513)
- Kayo Fujimoto (ORCID: https://orcid.org/0000-0002-8445-2711)
- Xinyue Hu (ORCID: https://orcid.org/0000-0002-2508-121X)
- Cui Tao (ORCID: https://orcid.org/0000-0002-4267-1924)
- Jingcheng Du (ORCID: https://orcid.org/0000-0002-0322-4566)
- Yang Xiang (ORCID: https://orcid.org/0000-0001-5252-0831)
- Evan Yu (ORCID: https://orcid.org/0000-0001-9135-3757)
Institutions
- Chicago Department of Public Health (US)
- University of Chicago (US)
- Mayo Clinic in Florida (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Annals of Medicine
- Published
- 2024-10-17
- DOI
- https://doi.org/10.1080/07853890.2024.2407063
- Citations
- 6
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.08
Funders
- National Institutes of Health