Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild
This addresses the need for interpretable, data-driven methods in diabetes management, offering a nuanced tool for comparing glycemic control, though it is incremental as it builds on existing mechanistic and data-driven approaches.
The paper tackled the problem of capturing individual variability in glycemic control at the meal level by proposing a hybrid variational autoencoder that learns interpretable representations from CGM and meal data, resulting in embeddings that produce clusters up to 4x better than existing methods.
Diabetes encompasses a complex landscape of glycemic control that varies widely among individuals. However, current methods do not faithfully capture this variability at the meal level. On the one hand, expert-crafted features lack the flexibility of data-driven methods; on the other hand, learned representations tend to be uninterpretable which hampers clinical adoption. In this paper, we propose a hybrid variational autoencoder to learn interpretable representations of CGM and meal data. Our method grounds the latent space to the inputs of a mechanistic differential equation, producing embeddings that reflect physiological quantities, such as insulin sensitivity, glucose effectiveness, and basal glucose levels. Moreover, we introduce a novel method to infer the glucose appearance rate, making the mechanistic model robust to unreliable meal logs. On a dataset of CGM and self-reported meals from individuals with type-2 diabetes and pre-diabetes, our unsupervised representation discovers a separation between individuals proportional to their disease severity. Our embeddings produce clusters that are up to 4x better than naive, expert, black-box, and pure mechanistic features. Our method provides a nuanced, yet interpretable, embedding space to compare glycemic control within and across individuals, directly learnable from in-the-wild data.