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

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 and survey-based data to predict phenotypes among adults, thus supporting clinical decision-making and personalized care.

METHODS

Demographic, clinical, lifestyle, and genetic family history data of 4408 adults revealed how capturing essential relationships in patient data from different sources can transform individual-level representations into multiple, complementary views. Evaluation of the predictive efficacy of the proposed method in comparison with 8 prevalent methods relied on recall, precision, F1-score, and area under the curve values. Moreover, a Shapley additive explanation analysis was performed for interpretability.

RESULTS

The proposed method consistently and significantly outperformed all benchmark methods. It attained the highest F1-score, showing a 32.8% improvement for nondiabetic MAFLD (0.531 vs 0.400) and 30.4% improvement for diabetic MAFLD (0.519 vs 0.398) over the respective best-performing benchmark. The results underscore the clinical value and utility of integrating clinical and survey-based data in the prediction of MAFLD phenotypes among adults.

CONCLUSIONS

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.