Transferable Evidence Reconstruction for Longitudinal Glucose Representations

2026-09-23

AI Summary

研究通过引入可转移证据重构方法解决长时间血糖监测数据中预测信息提取问题，利用自监督学习和对比学习提升连续血糖监测任务性能。

Abstract

Long physiological recordings contain many routine measurements, while predictive information is often concentrated in rare events, sustained burden, and recurring temporal patterns. Masked autoencoding recovers measurements; contrastive learning aligns views. We study self-supervision that explicitly prioritizes structured signal evidence. We introduce transferable evidence reconstruction (TER), which constructs evidence from unlabeled recordings, fits a fresh low-capacity reader on one recording group, and requires that reader to recover the same evidence in another group without refitting. Differentiating through this cross-group test learns representations with transferable evidence-decoding rules; the evidence guides self-supervision but is not used as a downstream feature. For continuous glucose monitoring (CGM), an observation-aware daily encoder and clock-aware multi-day memory bind glucose level and change to recorded time while organizing up to seven days of history. On the 14-task leaderboard, TER improves the strongest prior overall PR-AUC/ROC-AUC/Macro-F1 scores by 5.51/4.43/2.80 percentage points and sets a new best metric on 12/14 tasks. These leaderboard gains are 2.0-2.9 times the respective gaps between the two strongest baselines. With public pretraining data, folds, and the linear probe matched, TER outperforms our GlucoFM reproduction by 6.09/5.52/2.72 points. Target-reader ablations, same-history controls, and cross-person readouts support the combination of structured evidence, cross-group reader fitting, and learned multi-day organization.

Problem

• Longitudinal Glucose
• Predictive Information
• Structured Signal Evidence
• Continuous Glucose Monitoring (CGM)
• Self-Supervision

Innovation

• Transferable Evidence Reconstruction
• Cross-group Reader Fitting
• Continuous Glucose Monitoring (CGM)
• Structured Signal Evidence
• Self-supervision