Transferable Evidence Reconstruction for Longitudinal Glucose Representations
cs.LG
Submitted: 2026-09-23
Updated: 2026-09-25
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: Long physiological recordings contain many routine measurements, while predictive information often lies in rare events, sustained burden, and recurring patterns.
Terminology
Abstract
Long physiological recordings contain many routine measurements, while predictive information often lies in rare events, sustained burden, and recurring patterns. These properties can be computed as label-free evidence, but directly using them as features leaves limited labeled data to separate reproducible associations from sample-specific ones. Learning to reconstruct evidence can exploit unlabeled recordings, yet joint reconstruction does not explicitly require the decoding rule to transfer across individuals. We introduce transferable evidence reconstruction (TER): a Ridge regressor fits evidence from representations in one group and predicts it in an identity-disjoint group without refitting. The transfer error trains the encoder through the differentiable fit. For continuous glucose monitoring (CGM), clock-aware encoding preserves the multi-day content and timing needed for evidence recovery. Matched interventions connect the gains to reduced fitting-group sensitivity, with structured targets improving on raw recovery. Across ten leading CGM and time-series baselines, TER sets a new best metric on 12/14 phenotype tasks and exceeds the strongest prior overall PR-AUC/ROC-AUC/Macro-F1 by 4.95/4.43/0.66 percentage points; the PR-AUC and ROC-AUC gains are 2.6 times and 2.2 times the respective gaps between the two strongest baselines. Meal-response and future-CGM studies further demonstrate predictive utility. TER thus uses meaningful signal properties to supervise not only what a representation preserves, but how reliably it can be read across individuals.
Sources
- Meta-learning with differentiable closed-form solvers
- Self-Supervised Dynamical System Representations for Physiological Time-Series
- Unsupervised Learning via Meta-Learning
- Unsupervised Meta-Learning For Few-Shot Image Classification
- GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring
- CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining
- STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables
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