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) |
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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