Predicting Metabolic Dysfunction–Associated Fatty Liver Disease Phenotypes Among Adults: 2-Stage Contrastive Learning Method
The proposed method is a viable approach for MAFLD phenotype prediction. It is more effective in identifying at-risk adults than many prevalent data-driven analytics methods and thereby can enhance clinical decision-making and support patient-centric care and management.
Authors
- Paul Jen‐Hwa Hu (ORCID: https://orcid.org/0000-0002-4981-895X)
- Derek Hu (ORCID: https://orcid.org/0000-0003-4720-2622)
- Da Xu (ORCID: https://orcid.org/0000-0001-9433-0905)
- Ting Huang (ORCID: https://orcid.org/0000-0002-2932-6878)
- Sizhe Jasmine Chen (ORCID: https://orcid.org/0009-0004-7858-4402)
Institutions
- Chang Gung University (TW)
- University of Utah (US)
- Jen-Ai Hospital (TW)
- University of Mississippi (US)
- Keelung Chang Gung Memorial Hospital (TW)
- California State University, Long Beach (US)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-10-23
- DOI
- https://doi.org/10.2196/75747
- Citations
- 1
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35