基于Mamba的可解释血糖预测模型，支持个性化糖尿病管理。
SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Management
- 用Mamba架构融合血糖与穿戴设备信号进行预测
- 短期预测精度优于时序融合变压器基线模型
- 支持生理信号变化的反事实模拟，适合临床医生使用
连续血糖监测（CGM）生成密集数据流，对糖尿病管理至关重要，但现有预测模型缺乏可解释性。我们提出SSM-CGM，一种基于Mamba的神经状态空间预测模型，融合了来自AI-READI队列的CGM与可穿戴活动信号。该模型在短期预测上优于时序融合变压器基线，通过变量选择与时间归因提升可解释性，并能模拟计划内生理信号（如心率、呼吸）变化对近期内血糖的影响。这些特性使SSM-CGM成为可解释、生理学基础扎实的个性化糖尿病管理框架。
原文摘要 · Abstract (English)
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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