Multiple-input subject-specific modeling of plasma glucose concentration for feedforward control

Date
2014-01-01
Authors
Kotz, Kaylee
Rollins, Derrick K
Cinar, Ali
Mei, Yong
Roggendorf, Amy
Littlejohn, Elizabeth
Quinn, Laurie
Rollins, Derrick
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Abstract

The ability to accurately develop subject-specific, input causation models, for blood glucose concentration (BGC) for large input sets can have a significant impact on tightening control for insulin dependent diabetes. More specifically, for Type 1 diabetics (T1Ds), it can lead to an effective artificial pancreas (i.e., an automatic control system that delivers exogenous insulin) under extreme changes in critical disturbances. These disturbances include food consumption, activity variations, and physiological stress changes. Thus, this paper presents a free-living, outpatient, multiple-input, modeling method for BGC with strong causation attributes that is stable and guards against overfitting to provide an e ffective modeling approach for feedforward control (FFC). This approach is a Wiener block-oriented methodology, which has unique attributes for meeting critical requirements for effective, long-term, FFC.

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<p>Reprinted (adapted) with permission from <em>Industrial and Engineering Chemistry Research</em> 53 (2104): 18216, doi: <a href="http://dx.doi.org/10.1021/ie404119b" target="_blank">10.1021/ie404119b</a>. Copyright 2014 American Chemical Society.</p>
Keywords
Statistics, artificial organs, automation, food supply, glucose, insulin, artificial pancreas, blood glucose concentration, causation model, food consumption, omsi;om-dependent diabetes, multiple inputs, physiological stress, feedforward control
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