Data assimilation of glucose dynamics for use in the intensive care unit.

Madineh Sedigh-Sarvestani; David J. Albers; Bruce J. Gluckman

Data assimilation of glucose dynamics for use in the intensive care unit.

Madineh Sedigh-Sarvestani, David J. Albers, Bruce J. Gluckman

Research output: Contribution to journal › Article › peer-review

Abstract

We know much about the glucose regulatory system, yet the application of this knowledge is limited because simultaneous measurements of insulin and glucose are difficult to obtain. We present a data assimilation framework for combining sparse measurements of the glucose regulatory system, available in the intensive care unit setting, with a nonlinear computational model to estimate unmeasured variables and unknown parameters. We also demonstrate a method for choosing the best variables for measurement. We anticipate that this framework will improve glucose maintenance therapies and shed light on the underlying biophysical process.

Original language	English (US)
Pages (from-to)	5437-5440
Number of pages	4
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
State	Published - 2012

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition
Signal Processing
Biomedical Engineering
Health Informatics

Cite this

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abstract = "We know much about the glucose regulatory system, yet the application of this knowledge is limited because simultaneous measurements of insulin and glucose are difficult to obtain. We present a data assimilation framework for combining sparse measurements of the glucose regulatory system, available in the intensive care unit setting, with a nonlinear computational model to estimate unmeasured variables and unknown parameters. We also demonstrate a method for choosing the best variables for measurement. We anticipate that this framework will improve glucose maintenance therapies and shed light on the underlying biophysical process.",

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AB - We know much about the glucose regulatory system, yet the application of this knowledge is limited because simultaneous measurements of insulin and glucose are difficult to obtain. We present a data assimilation framework for combining sparse measurements of the glucose regulatory system, available in the intensive care unit setting, with a nonlinear computational model to estimate unmeasured variables and unknown parameters. We also demonstrate a method for choosing the best variables for measurement. We anticipate that this framework will improve glucose maintenance therapies and shed light on the underlying biophysical process.

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JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

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ER -

Data assimilation of glucose dynamics for use in the intensive care unit.

Abstract

All Science Journal Classification (ASJC) codes

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