Data, Models, and Scripts of Integrated Physiological Model of Virtual Diabetic Patient for Machine Learning Research

Machine Learning (ML) based predictive models of blood glucose forecasting for diabetic patient produce future glucose trend without any physiological explanation. In this research effort, operation research (OR) is adopted by building a constraint-based physiological model of a diabetic patient with exercise dynamics for producing explanation from ML-based forecasting. The proposed constraint-based diabetic model is implemented in MATLAB/Simulink environment and validated with clinical and free-living datasets. Then the model is applied for OR on forecasting of a Feed-Forward Neural Network (FFNN) which is built on continuous glucose monitoring (CGM) profile along with carbohydrate and physical activity data of a type-2 diabetic patient.

This material set contains all Simulink model files, MATLAB scripts, the experimental datasets used for building and validating physiological and ML models, and result files.

基于机器学习（ML）的血糖预测模型，针对糖尿病患者，能够在不提供任何生理解释的情况下预测未来的血糖趋势。在本项研究工作中，通过构建一个基于约束的糖尿病患者生理模型，并纳入运动动力学因素，采用运筹学（OR）方法，以从基于机器学习的预测中提取解释。所提出的基于约束的糖尿病模型在 MATLAB/Simulink 环境中实现，并通过临床和自由生活数据集进行验证。随后，该模型被应用于运筹学领域，以预测前馈神经网络（FFNN）的输出，该神经网络基于连续血糖监测（CGM）数据以及2型糖尿病患者的碳水化合物和身体活动数据构建。本数据集包含所有 Simulink 模型文件、MATLAB 脚本、构建和验证生理和机器学习模型所使用的实验数据集，以及结果文件。