Predicting Metabolic Dysfunction–Associated Fatty Liver Disease Phenotypes Among Adults: 2-Stage Contrastive Learning Method
Bibliographic record
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 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".