SSMCGM-Stream: A Streaming State-Space Model for Personalized Diabetes Management

Key points

• Continuous glucose monitoring supports diabetes management but requires forecasting models that are accurate, interpretable, and efficient enough for real-time deployment.1
• Built on this foundation, SSMCGM-Agent delivers risk-aware, natural-language decision support by translating forecasts, explanations, and counterfactuals into grounded recommendations through a guarded language-model interface.1
• Evaluated on the T1DEXI cohort, SSMCGM-Stream achieves forecasting performance comparable to strong neural baselines while substantially reducing computational cost, enabling continuous on-device diabetes decision support.1

Sources (1)

• [1]SSMCGM-Stream: A Streaming State-Space Model for Personalized Diabetes Management
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 10:48 PM

  We present SSMCGM-Stream, a streaming state-space model that maintains a personalized hidden state updated with each incoming glucose reading to provide constant-time forecasting, physiologically structured counterfactual reasoning, and interpretable forecasts through hidden-state attribution.

  Continuous glucose monitoring supports diabetes management but requires forecasting models that are accurate, interpretable, and efficient enough for real-time deployment.

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