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 model...

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Bibliographic Details
Published inarXiv.org
Main Authors Asbjørn Thode Reenberg, Ritschel, Tobias K S, Dammann, Bernd, Jørgensen, John Bagterp
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.02.2022
Subjects
Clinical trials
Diabetes
Hardware
Mathematical models
Monte Carlo simulation
Nonlinear control
Predictive control
Software
Uncertainty
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Summary: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.
ISSN:2331-8422