Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild

• URL: http://arxiv.org/abs/2312.03344v1
• Date: Wed, 6 Dec 2023 08:36:23 GMT
• Title: Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild
• Authors: Ke Alexander Wang, Emily B. Fox
• Abstract summary: 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. Our embeddings produce clusters that are up to 4x better than naive, expert, black-box, and pure mechanistic features.
• Score: 10.240619571788786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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