A Real-Time Digital Twin for Type 1 Diabetes using Simulation-Based Inference
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Abstract (arXiv)
Accurately estimating parameters of physiological models is essential to achieving reliable digital twins. For Type 1 Diabetes, this is particularly challenging due to the complexity of glucose-insulin interactions. Traditional methods based on Markov Chain Monte Carlo struggle with high-dimensional parameter spaces and fit parameters from scratch at inference time, making them slow and computationally expensive. In this study, we propose a Simulation-Based Inference approach based on Neural Posterior Estimation to efficiently capture the complex relationships between meal intake, insulin, and glucose level, providing faster, amortized inference. Our experiments demonstrate that SBI not only outperforms traditional methods in parameter estimation but also generalizes better to unseen conditions, offering real-time posterior inference with reliable uncertainty quantification.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 2 Jul 2025.
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8 of 21 references.
References · 8
- 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
- 2017Masked Autoregressive Flow for Density Estimationcited by 684
- 2019The frontier of simulation-based inferencecited by 502
- 2017Flexible statistical inference for mechanistic models of neural dynamicscited by 163
- 2021Real-time gravitational-wave science with neural posterior estimationcited by 78
- 2016Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimationcited by 52
- 2021A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excesscited by 12
- 2023Simulation-based Inference for Cardiovascular Modelscited by 11
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Citing papers per year: 2026: 1.
1 paper.