SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Management

Shakson Isaac, Yentl Collin, Chirag Patel

Keywords: Diabetes, CGM, Wearables, Mamba, Interpretability, Counterfactuals

TL;DR: We introduce SSM-CGM, an interpretable Mamba-based model that improves glucose forecasting from wearables, reveals what and when features matter, and enables counterfactual simulations for personalized diabetes management.

Abstract: Continuous glucose monitoring (CGM) generates dense data streams critical for diabetes management, but most used forecasting models lack interpretability for clinical use. We present SSM-CGM, a Mamba-based neural state-space forecasting model that integrates CGM and wearable activity signals from the AI-READI cohort. SSM-CGM improves short-term accuracy over a Temporal Fusion Transformer baseline, adds interpretability through variable selection and temporal attribution, and enables counterfactual forecasts simulating how planned changes in physiological signals (e.g., heart rate, respiration) affect near-term glucose. Together, these features make SSM-CGM an interpretable, physiologically grounded framework for personalized diabetes management.

Submission Number: 48