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

• URL: http://arxiv.org/abs/2510.04386v1
• Date: Sun, 05 Oct 2025 22:37:28 GMT
• Title: SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Management
• Authors: Shakson Isaac, Yentl Collin, Chirag Patel,
• Abstract summary: Continuous glucose monitoring (CGM) generates dense data streams critical for diabetes management.<n>We present SSM-CGM, a Mamba-based neural state-space forecasting model that integrates CGM and wearable activity signals.
• Score: 0.6481500397175589
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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