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Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI. | LitMetric

AI Article Synopsis

  • Type 1 diabetes mellitus (T1DM) involves issues with insulin production and blood sugar control, and current solutions like the artificial pancreas (AP) fall short in managing postprandial glucose response (PGR) due to limited data on influencing factors.
  • This study focuses on using deep neural network (DNN) models to analyze various meal-related factors, including nutrient intake and blood glucose levels before meals, to better predict post-meal blood glucose levels at different time intervals.
  • By employing explainable AI methods, the research aims to clarify how each factor affects predictions, potentially leading to improved decision-support tools for better PGR management for T1DM patients and advancements in AP technology for enhanced quality of life.

Article Abstract

Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates basal insulin delivery and glucose monitoring. However, APs are unable to manage postprandial glucose response (PGR) due to limited knowledge of its determinants, requiring additional information for accurate bolus delivery, such as estimated carbohydrate intake. This study aims to quantify the influence of various meal-related factors on predicting postprandial blood glucose levels (BGLs) at different time intervals (15 min, 60 min, and 120 min) after meals by using deep neural network (DNN) models. The prediction models incorporate preprandial blood glucose values, insulin dosage, and various meal-related nutritional factors such as intake of energy, carbohydrates, proteins, lipids, fatty acids, fibers, glycemic index, and glycemic load as input variables. The impact of input features was assessed by exploiting eXplainable Artificial Intelligence (XAI) methodologies, specifically SHapley Additive exPlanations (SHAP), which provide insights into each feature's contribution to the model predictions. By leveraging XAI methodologies, this study aims to enhance the interpretability and transparency of BGL prediction models and validate clinical literature hypotheses. The findings can aid in the development of decision-support tools for individuals with T1DM, facilitating PGR management and reducing the risks of adverse events. The improved understanding of PGR determinants may lead to advancements in AP technology and improve the overall quality of life for T1DM patients.

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Source
http://dx.doi.org/10.1109/JBHI.2023.3348334DOI Listing

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