Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High-performance Uncertainty Quantification in Large-scale Virtual
  Clinical Trials of Closed-loop Diabetes Treatment   In this paper, we propose a virtual clinical trial for assessing the
performance and identifying risks in closed-loop diabetes treatments. Virtual
clinical trials enable fast and risk-free tests of many treatment variations
for large populations of fictive patients (represented by mathematical models).
We use closed-loop Monte Carlo simulation, implemented in high-performance
software and hardware, to quantify the uncertainty in treatment performance as
well as to compare the performance in different scenarios or of different
closed-loop treatments. Our software can be used for testing a wide variety of
control strategies ranging from heuristical approaches to nonlinear model
predictive control. We present an example of a virtual clinical trial with one
million patients over 52 weeks, and we use high-performance software and
hardware to conduct the virtual trial in 1 h and 22 min.

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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: plasm-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex