Hybrid digital-twin matching plus Seq2Seq LSTM reduces 240-minute glucose forecast bias by 13.89 mg/dL and MAE by 28.62 mg/dL versus baselines on held-out T1DEXI data.
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A simulation-driven digital twin framework is shown to generate interpretable diabetes trajectories for decision-aware analysis by combining benchmark data with controlled synthetic scenarios.
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PhysioSeq2Seq: A Hybrid Physiological Digital Twin and Sequence-to-Sequence LSTM for Long-Horizon Glucose Forecasting in Type 1 Diabetes
Hybrid digital-twin matching plus Seq2Seq LSTM reduces 240-minute glucose forecast bias by 13.89 mg/dL and MAE by 28.62 mg/dL versus baselines on held-out T1DEXI data.
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A Proof-of-Concept Simulation-Driven Digital Twin Framework for Decision-Aware Diabetes Modeling
A simulation-driven digital twin framework is shown to generate interpretable diabetes trajectories for decision-aware analysis by combining benchmark data with controlled synthetic scenarios.