The long term objective of this work is to develop new algorithmic approaches to optimize the delivery of insulin in an automated fashion to people with type 1 diabetes. Specifically, novel approaches to patient characterization will be developed to predict glucose profiles under various conditions of stress, exploiting developments in pattern recognition from the engineering literature. The net result will be the development of an algorithm that predicts the dosages of insulin delivered to the patient by the clinical team. This will involve the identification of recurring patterns of glucose response to meal and other stimuli. The algorithm will be tested in both simulation and clinical trials for varying degrees of patient stress and meal stimuli, as well as robustness to sensor noise and patient characterization uncertainty.
The specific aims of this project are to: i) characterize a group of type 1 diabetic patients in terms of their glucose profiles, ii) develop algorithms for insulin dosing based on pattern recognition, iii) mimic the dual phase of insulin secretion related to meals through post-meal regulation of insulin infusion in an inpatient setting, iv) repeat the methods under pharmacologically-induced stress states and following exercise, and v) develop advanced model-based control strategy for glucose regulation under conditions of type 1 diabetes.
The aims will blend prototype algorithms that are drawn from systems engineering with validation in a series of clinical tests. The proposed collaboration between systems engineers and renowned diabetes researchers in an established clinical research setting will allow a novel fusion of methods that can be truly characterized as """"""""bench to bedside"""""""". The medical collaborators in the proposal are located at the prestigious Sansum Diabetes Research Institute, which is located less than 10 miles from the campus of the University of California, Santa Barbara. The exchange of personnel will be facilitated, allowing the student and post-doc supported on this project to work at both the institute and the University.
Harvey, Rebecca A; Wang, Youqing; Grosman, Benyamin et al. (2010) Quest for the artificial pancreas: combining technology with treatment. IEEE Eng Med Biol Mag 29:53-62 |
Finan, Daniel A; Zisser, Howard; Jovanovi?, Lois et al. (2010) Automatic Detection of Stress States in Type 1 Diabetes Subjects in Ambulatory Conditions. Ind Eng Chem Res 49:7843-7848 |
Finan, Daniel A; Doyle 3rd, Francis J; Palerm, Cesar C et al. (2009) Experimental evaluation of a recursive model identification technique for type 1 diabetes. J Diabetes Sci Technol 3:1192-202 |
Marchetti, Gianni; Barolo, Massimiliano; Jovanovic, Lois et al. (2008) A Feedforward-Feedback Glucose Control Strategy for Type 1 Diabetes Mellitus. J Process Control 18:149-162 |
Dassau, Eyal; Bequette, B Wayne; Buckingham, Bruce A et al. (2008) Detection of a meal using continuous glucose monitoring: implications for an artificial beta-cell. Diabetes Care 31:295-300 |
Marchetti, Gianni; Barolo, Massimiliano; Jovanovic, Lois et al. (2008) An improved PID switching control strategy for type 1 diabetes. IEEE Trans Biomed Eng 55:857-65 |
Finan, Daniel A; Zisser, Howard; Jovanovic, Lois et al. (2007) Practical issues in the identification of empirical models from simulated type 1 diabetes data. Diabetes Technol Ther 9:438-50 |
Marchetti, Gianni; Barolo, Massimiliano; Jovanovic, Lois et al. (2006) An improved PID switching control strategy for type 1 diabetes. Conf Proc IEEE Eng Med Biol Soc 1:5041-4 |
Palerm, Cesar C; Rodriguez-Fernandez, Maria; Bevier, Wendy C et al. (2006) Robust parameter estimation in a model for glucose kinetics in type 1 diabetes subjects. Conf Proc IEEE Eng Med Biol Soc 1:319-22 |
Owens, Camelia; Zisser, Howard; Jovanovic, Lois et al. (2006) Run-to-run control of blood glucose concentrations for people with Type 1 diabetes mellitus. IEEE Trans Biomed Eng 53:996-1005 |