Learning Insulin-Glucose Dynamics in the Wild

TL;DR

This paper introduces a hybrid insulin-glucose model combining physiological insights with machine learning to improve blood glucose forecasting in type 1 diabetics, especially over long time horizons.

Contribution

It develops a novel time-varying model that maintains interpretability while leveraging pattern recognition, outperforming less constrained models like LSTM.

Findings

Improved long-term blood glucose forecasts up to six hours.

Model maintains physiological plausibility and interpretability.

Flexible, structured representations of subject variability are crucial.

Abstract

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.