A Real-Time Digital Twin for Type 1 Diabetes using Simulation-Based Inference

TL;DR

This paper introduces a real-time digital twin for Type 1 Diabetes that uses simulation-based inference with neural networks to efficiently estimate physiological parameters, enabling faster and more reliable glucose level predictions.

Contribution

It presents a novel application of neural posterior estimation for real-time parameter inference in diabetes modeling, improving speed and accuracy over traditional methods.

Findings

Outperforms traditional inference methods in accuracy

Provides real-time, amortized inference with uncertainty quantification

Generalizes well to unseen conditions

Abstract

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.