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.