Predicting Metabolic Dysfunction-Associated Fatty Liver Disease Phenotypes Among Adults: 2-Stage Contrastive Learning Method.

ABSTRACT:

Background

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a leading cause of chronic disease and can progress to liver fibrosis or hepatocellular carcinoma. Its subtypes-obese, diabetic, and lean-are associated with varying degrees of fibrotic burden and different complications, yet the existing analytics methods often overlook its multisystem nature, intraphenotype variability, and disease dynamics. These limitations hinder accurate risk stratification and restrict personalized intervention planning.

Objective

This study developed a novel, 2-stage, contrastive learning-based method to predict the phenotype of MAFLD among adults. This method leverages multiview contrastive learning; it models individual heterogeneities and important relationships in clinical

SUBMITTER: Chen SJ 

PROVIDER: S-EPMC12702840 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature