Synthesis of insulin pump controllers from safety specifications using Bayesian model validation

Authors:
Sumit Kumar Jha;Raj Gautam Dutta;Christopher J. Langmead;Susmit Jha;Emily Sassano
Affiliations:
EECS Department, University of Central Florida, Orlando, FL 32816, USA;EECS Department, University of Central Florida, Orlando, FL 32816, USA;Computer Science Department and Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh PA, 15213, USA;EECS Department, UC Berkeley, CA 94703, USA;EECS Department, University of Central Florida, Orlando, FL 32816, USA
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2012

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Abstract

Insulin pump controllers seek to alleviate the chronic suffering caused by diabetes that affects over 6% of the world population. The design of control laws for insulin pump controllers has been well studied. However, the parameters involved in the control law are difficult to synthesize. Traditionally, ad hoc approaches using animal models and random sampling have been used to construct these parameters. We suggest a synthesis algorithm that uses Bayesian statistical model validation to reduce the number of simulations needed. We apply this algorithm to the problem of insulin pump controller synthesis using in silico simulation of the glucose-insulin metabolism model.