RankPPGR: automatic diet monitoring through rank learning

Ghady Nasrallah, Anurag Das, Sicong Huang, Bobak J Mortazavi, Ricardo Gutierrez-Osuna

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Keywords: Diet monitoring, continuous glucose monitors, rank learning

TL;DR: We present a rank-learning model that can discriminate meals with low- and high-glycemic loads.

Abstract: Managing diabetes requires careful monitoring of food intake, yet manual logging is burdensome and error-prone. Prior research has shown that the macronutrient composition of a meal (e.g., carbohydrates, protein, fat, and fiber) can be inferred from its postprandial glucose response (PPGR). However, this is a challenging problem given the large inter- individual differences in PPGRs, and the complex interaction between macronutrients in mixed meals. To address these issues, we propose RankPPGR, a rank-learning framework that analyzes within-subject differences in pairwise PPGRs from meals with varying macronutrient composition, and learns a non-linear embedding of meal macronutrients that reflects their combined influence in glycemic responses. We also propose a few-shot regression module that uses outputs from RankPPGR to infer macronutrient composition using a limited number of labeled meals per individual. We evaluate the model on an experimental dataset containing PPGRs to mixed meals from 45 participants. RankPPGR significantly improves both pairwise classification and macronutrient inference performance over a sample-based regression baseline.

Track: 7. General Track

Registration Id: DQNWN52L6J4

Submission Number: 306