Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates
This work addresses the challenge of personalizing glucose-insulin dynamics models for individuals in real-world settings, though it is incremental as it builds on existing differential equation frameworks.
The paper tackled the problem of modeling glucose absorption rates after meals in daily life by learning from macronutritional data and meal covariates using a neural network integrated into a differential equation, resulting in better forecasts than heuristic methods on simulated data.
Traditional models of glucose-insulin dynamics rely on heuristic parameterizations chosen to fit observations within a laboratory setting. However, these models cannot describe glucose dynamics in daily life. One source of failure is in their descriptions of glucose absorption rates after meal events. A meal's macronutritional content has nuanced effects on the absorption profile, which is difficult to model mechanistically. In this paper, we propose to learn the effects of macronutrition content from glucose-insulin data and meal covariates. Given macronutrition information and meal times, we use a neural network to predict an individual's glucose absorption rate. We use this neural rate function as the control function in a differential equation of glucose dynamics, enabling end-to-end training. On simulated data, our approach is able to closely approximate true absorption rates, resulting in better forecast than heuristic parameterizations, despite only observing glucose, insulin, and macronutritional information. Our work readily generalizes to meal events with higher-dimensional covariates, such as images, setting the stage for glucose dynamics models that are personalized to each individual's daily life.