# Learning Insulin-Glucose Dynamics in the Wild

arXiv: https://arxiv.org/abs/2008.02852 · HTML: https://mlsanity.com/paper/2008.02852/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 15 of 15)

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- [A Framework for Machine Learning of Model Error in Dynamical Systems](https://mlsanity.com/paper/2107.06658/) (2021)
- [Hybrid2 Neural ODE Causal Modeling and an Application to Glycemic Response](https://mlsanity.com/paper/2402.17233/) (2024)
- [Breiman's two cultures: You don't have to choose sides](https://mlsanity.com/paper/2104.12219/) (2021)
- [Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose](https://mlsanity.com/paper/2304.08593/) (2023)
- [Global Deep Forecasting with Patient-Specific Pharmacokinetics](https://mlsanity.com/paper/2309.13135/) (2023)
- [GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series](https://mlsanity.com/paper/2402.16230/) (2024)
- [HAD-Net: Hybrid Attention-based Diffusion Network for Glucose Level Forecast](https://mlsanity.com/paper/2111.07455/) (2021)
- [The Safety Challenges of Deep Learning in Real-World Type 1 Diabetes Management](https://mlsanity.com/paper/2310.14743/) (2023)
- [Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild](https://mlsanity.com/paper/2312.03344/) (2023)
- [A Multitask VAE for Time Series Preprocessing and Prediction of Blood Glucose Level](https://mlsanity.com/paper/2410.00015/) (2024)
- [Automatic and Structure-Aware Sparsification of Hybrid Neural ODEs](https://mlsanity.com/paper/2505.18996/) (2025)
- [Sharpness-Aware Hybrid Model Learning for Architecture-Agnostic Parameter Estimation](https://mlsanity.com/paper/2602.06837/) (2026)
- [Interventional Flow Matching: Prospective Dose-Response Forecasting with Velocity-Field Jacobian Regularization](https://mlsanity.com/paper/2606.29386/) (2026)

## Abstract (arXiv)

We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics. We augment an existing biomedical model by introducing time-varying dynamics driven by a machine learning sequence model. Our model maintains a physiologically plausible inductive bias and clinically interpretable parameters -- e.g., insulin sensitivity -- while inheriting the flexibility of modern pattern recognition algorithms. Critical to modeling success are the flexible, but structured representations of subject variability with a sequence model. In contrast, less constrained models like the LSTM fail to provide reliable or physiologically plausible forecasts. We conduct an extensive empirical study. We show that allowing biomedical model dynamics to vary in time improves forecasting at long time horizons, up to six hours, and produces forecasts consistent with the physiological effects of insulin and carbohydrates.