Neural posterior estimation infers 17 diabetes-model parameters and initial conditions from CGM data in 3.4 seconds, with better out-of-sample glucose forecasts than MCMC and MAP baselines in simulated tests.
IEEE Transactions on Biomedical Engineering60(12), 3524–3533 (2013)
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A Real-Time Digital Twin for Type 1 Diabetes using Simulation-Based Inference
Neural posterior estimation infers 17 diabetes-model parameters and initial conditions from CGM data in 3.4 seconds, with better out-of-sample glucose forecasts than MCMC and MAP baselines in simulated tests.