RINet: synthetic data training for indirect estimation of clinical reference distributions
Synthetic data training is a viable approach for developing accurate indirect RI estimation models for both univariate and bivariate clinical data. This strategy could help address some limitations of real-world data, direct analyses, and univariate RIs.
Authors
- Julian Velev (ORCID: https://orcid.org/0000-0002-2509-7617)
- Jack LeBien (ORCID: https://orcid.org/0000-0001-7652-4124)
- Abiel Roche-Lima
Institutions
- University of Puerto Rico, Medical Sciences Campus (PR)
- University of Puerto Rico System (PR)
- Puerto Rico Department of Health (PR)
- Abt Global (United States) (US)
Publication Details
- Journal
- Journal of Biomedical Informatics
- Published
- 2026-01-08
- DOI
- https://doi.org/10.1016/j.jbi.2026.104980
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 104.19