Developing personalized empirical models for Type-I diabetes: An extended Kalman filter approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

An essential component of insulin therapy for type 1 diabetes involves the prediction of blood glucose levels as function of exogenous perturbations such as insulin dose and meal intake. Fluctuations in blood glucose are generated by a complex biophysical system and have demonstrated substantial variation at different times of a day within a subject and between subjects. In this paper, we present a new data-driven dynamic model with time-varying coefficients that are used to explicitly quantify the time-varying patient-specific effects of insulin dose and meal intake on blood glucose fluctuations. Using the 3-variate time series of blood glucose level, insulin dose and meal intake of an individual type 1 diabetic subject, we apply an extended Kalman Filter technique to estimate time-varying coefficients of the patient-specific model. We evaluate our empirical model using a FDA-approved simulator with 30 patients. The model developed in this paper can be used in model-based control such as adaptive control and model predictive control of blood glucose by means of an artificial pancreas.

Original languageEnglish (US)
Title of host publication2013 American Control Conference, ACC 2013
Pages2923-2928
Number of pages6
StatePublished - Sep 11 2013
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: Jun 17 2013Jun 19 2013

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2013 1st American Control Conference, ACC 2013
CountryUnited States
CityWashington, DC
Period6/17/136/19/13

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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