CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining
Summary
Introduces CGM-JEPA, a self-supervised pretraining framework for continuous glucose monitor data that improves cross-modal and cross-cohort performance through masked latent prediction and distributional objectives.
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Paper page - CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining
Source: https://huggingface.co/papers/2605.00933
Abstract
A self-supervised pretraining framework for continuous glucose monitoring data achieves superior cross-modal and cross-cohort performance by predicting masked latent representations and incorporating cross-view distributional objectives.
Continuous Glucose Monitoring (CGM) can detect early metabolic subphenotypes (insulin resistance, IR; β-cell dysfunction), but population-scale deployment faces two coupled problems. First, the same physiological state appears through multiple views (CGM time series, venous OGTT, Glucodensity summaries), so single-view representations fail to transfer when deployment shifts the modality or setting. Second, baselines perform inconsistently across these shifts. Both problems point to one remedy: representations that abstract away from any single view to capture higher-level temporal and distributional structure. We propose CGM-JEPA, aself-supervised pretrainingframework which predictsmasked latent representationsrather than raw values, yielding abstraction that transfers across modalities. X-CGM-JEPA adds a masked Glucodensitycross-view objectivefor complementarydistributional information. We pretrain on sim389k unlabeled CGM readings from 228 subjects and evaluate on two clinical cohorts (N=27 and N=17 public-release subsets) across three regimes (cohort generalization,venous-to-CGM transfer, home CGM) under 20-iteration times 2-fold cross-validation. X-CGM-JEPA ranks first or second on AUROC for both endpoints across all three regimes while no baseline does, exceeding the strongest baseline by up to +6.5 pp incohort generalizationand +3.6 pp invenous-to-CGM transfer(paired Wilcoxon, p<0.001). Under modality shift, it matches mean AUROC while redistributing toward weaker subgroups (ethnicity AUROC gap shrinks 25-54%); on sparse in-domain venous data, the distributional view liftslabel-aware clustering(ARI+39%,NMI+40%). Code and weights: https://github.com/cruiseresearchgroup/CGM-JEPA
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