Annuzzi Giovanni, Apicella Andrea, Arpaia Pasquale, Bozzetto Lutgarda, Criscuolo Sabatina, De Benedetto Egidio, Pesola Marisa, Prevete Roberto
IEEE J Biomed Health Inform. 2024 May;28(5):3123-3133. doi: 10.1109/JBHI.2023.3348334. Epub 2024 May 6.
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.
1型糖尿病（T1DM）的特征是胰岛素缺乏和血糖控制问题。目前最先进的解决方案是人工胰腺（AP），它集成了基础胰岛素输送和葡萄糖监测功能。然而，由于对餐后血糖反应（PGR）的决定因素了解有限，人工胰腺无法管理餐后血糖反应，需要额外信息来准确进行大剂量胰岛素输送，例如估计的碳水化合物摄入量。本研究旨在通过使用深度神经网络（DNN）模型，量化各种与膳食相关的因素对预测餐后不同时间间隔（15分钟、60分钟和120分钟）的餐后血糖水平（BGL）的影响。预测模型将餐前血糖值、胰岛素剂量以及各种与膳食相关的营养因素作为输入变量，这些因素包括能量、碳水化合物、蛋白质、脂质、脂肪酸、纤维、血糖指数和血糖负荷的摄入量。通过利用可解释人工智能（XAI）方法，特别是SHapley加性解释（SHAP）来评估输入特征的影响，SHAP能深入了解每个特征对模型预测的贡献。通过利用XAI方法，本研究旨在提高BGL预测模型的可解释性和透明度，并验证临床文献假设。研究结果有助于为T1DM患者开发决策支持工具，促进PGR管理并降低不良事件风险。对PGR决定因素的更好理解可能会推动AP技术的进步，提高T1DM患者的整体生活质量。
