Title :
Personalized glucose-insulin metabolism model based on self-organizing maps for patients with Type 1 Diabetes Mellitus
Author :
Zarkogianni, K. ; Litsa, E. ; Vazeou, A. ; Nikita, Konstantina S.
Author_Institution :
Biomed. Simulations & Imaging Lab., Nat. Tech. Univ. of Athens, Athens, Greece
Abstract :
The present paper aims at the design, the development and the evaluation of a personalized glucose-insulin metabolism model for patients with Type 1 Diabetes Mellitus (T1DM). The personalized model is based on the combined use of Compartmental Models (CMs) and a Self Organizing Map (SOM). The model receives information related to previous glucose levels, subcutaneous insulin infusion rates and the time and amount of carbohydrates ingested. Previous glucose measurements along with the outputs of the CMs which simulate the sc insulin kinetics and the glucose absorption from the gut into the blood, respectively, are fed into the SOM which simulates glucose kinetics in order for the latter to provide with future glucose profile. The personalized model is evaluated using data from the medical records of 12 patients with T1DM for the time being on insulin pumps and CGMS. The obtained results demonstrate the ability of the proposed model to capture the metabolic behavior of a patient with T1DM and to handle intra- and inter-patient variability.
Keywords :
diseases; patient care; patient monitoring; self-organising feature maps; sugar; CGMS; SOM; T1DM; Type 1 Diabetes Mellitus patients; compartmental models; glucose absorption; glucose measurements; insulin pumps; interpatient variability; intrapatient variability; patient metabolic behavior; personalized glucose-insulin metabolism model; sc insulin kinetics; self-organizing maps; subcutaneous insulin infusion rates; Biochemistry; Biological system modeling; Diabetes; Insulin; Neurons; Sugar; Vectors;
Conference_Titel :
Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on
Conference_Location :
Chania
DOI :
10.1109/BIBE.2013.6701604