Simulation Software for Assessment of Nonlinear and Adaptive Multivariable Control Algorithms: Glucose - Insulin Dynamics in Type 1 Diabetes.
Mudassir RashidSediqeh SamadiMert SevilIman HajizadehPaul KolodziejNicole HobbsZacharie MaloneyRachel BrandtJianyuan FengMinsun ParkLaurie QuinnAli CinarPublished in: Computers & chemical engineering (2019)
A simulator for testing automatic control algorithms for nonlinear systems with time-varying parameters, variable time delays, and uncertainties is developed. It is based on simulation of virtual patients with Type 1 diabetes (T1D). Nonlinear models are developed to describe glucose concentration (GC) variations based on user-defined scenarios for meal consumption, insulin administration, and physical activity. They compute GC values and physiological variables, such as heart rate, skin temperature, accelerometer, and energy expenditure, that are indicative of physical activities affecting GC dynamics. This is the first simulator designed for assessment of multivariable controllers that consider supplemental physiological variables in addition to GC measurements to improve glycemic control. Virtual patients are generated from distributions of identified model parameters using clinical data. The simulator will enable testing and evaluation of new control algorithms proposed for automated insulin delivery as well as various control algorithms for nonlinear systems with uncertainties, time-varying parameters and delays.
Keyphrases
- glycemic control
- type diabetes
- machine learning
- deep learning
- blood glucose
- physical activity
- heart rate
- virtual reality
- end stage renal disease
- heart rate variability
- gas chromatography
- ejection fraction
- insulin resistance
- weight loss
- chronic kidney disease
- big data
- cardiovascular disease
- climate change
- newly diagnosed
- prognostic factors
- electronic health record
- high throughput
- data analysis
- metabolic syndrome
- single cell