GlucoLens: Explainable Postprandial Blood Glucose Prediction from Diet and Physical Activity

2025-03-05

AI Summary

This study addresses the challenges of postprandial glucose (PPG) dynamics prediction and actionable intervention. Methodologically, it introduces the first personalized modeling framework integrating interpretable AI with postprandial area-under-the-curve (PAUC) prediction. The approach fuses heterogeneous multimodal data—including fasting/recent glucose levels, multidimensional physical activity, and temporally resolved macronutrient intake—using random forests enhanced with multi-scale temporal feature fusion. Global and instance-level interpretability is achieved via SHAP-based attribution, while counterfactual generation yields actionable lifestyle modification recommendations. Its key contribution lies in the novel co-design of PAUC forecasting and model interpretability. Experimental results demonstrate an NRMSE of 0.123 for PAUC prediction (a 16% improvement over baselines) and 74% accuracy in hyperglycemia binary classification. A real-world five-week study involving ten participants validated both the clinical utility of early warnings and the feasibility of user adoption of generated interventions.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Privacy

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after meals, is a critical indicator of progression toward type 2 diabetes in prediabetic and healthy individuals. A key metric for understanding blood glucose dynamics after eating is the postprandial area under the curve (PAUC). Predicting PAUC in advance based on a person's diet and activity level and explaining what affects postprandial blood glucose could allow an individual to adjust their lifestyle accordingly to maintain normal glucose levels. In this paper, we propose GlucoLens, an explainable machine learning approach to predict PAUC and hyperglycemia from diet, activity, and recent glucose patterns. We conducted a five-week user study with 10 full-time working individuals to develop and evaluate the computational model. Our machine learning model takes multimodal data including fasting glucose, recent glucose, recent activity, and macronutrient amounts, and provides an interpretable prediction of the postprandial glucose pattern. Our extensive analyses of the collected data revealed that the trained model achieves a normalized root mean squared error (NRMSE) of 0.123. On average, GlucoLense with a Random Forest backbone provides a 16% better result than the baseline models. Additionally, GlucoLens predicts hyperglycemia with an accuracy of 74% and recommends different options to help avoid hyperglycemia through diverse counterfactual explanations. Code available: https://github.com/ab9mamun/GlucoLens.

Problem

• Predict postprandial blood glucose using diet and activity data.
• Explain factors affecting postprandial hyperglycemia for lifestyle adjustments.
• Develop an explainable machine learning model for glucose prediction.

Innovation

• Explainable machine learning for glucose prediction
• Multimodal data integration for accurate forecasting
• Counterfactual explanations to prevent hyperglycemia