Literature DB >> 20870308

Comparison of interval and Monte Carlo simulation for the prediction of postprandial glucose under uncertainty in type 1 diabetes mellitus.

R Calm1, M García-Jaramillo, J Bondia, M A Sainz, J Vehí.   

Abstract

In this paper, the problem of tackling uncertainty in the prediction of postprandial blood glucose is analyzed. Two simulation approaches, Monte Carlo and interval models, are studied and compared. Interval simulation is carried out using modal interval analysis. Simulation of a glucoregulatory model with uncertainty in insulin sensitivities, glucose absorption and food intake is carried out using both methods. Interval simulation is superior in predicting all severe and mild hyper- and hypoglycemia episodes. Furthermore, much less computational time is required for interval simulation than for Monte Carlo simulation. Copyright Â
© 2010 Elsevier Ireland Ltd. All rights reserved.

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Year:  2010        PMID: 20870308     DOI: 10.1016/j.cmpb.2010.08.008

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  4 in total

1.  A simple robust method for estimating the glucose rate of appearance from mixed meals.

Authors:  Pau Herrero; Jorge Bondia; Cesar C Palerm; Josep Vehí; Pantelis Georgiou; Nick Oliver; Christofer Toumazou
Journal:  J Diabetes Sci Technol       Date:  2012-01-01

2.  Improving the computational effort of set-inversion-based prandial insulin delivery for its integration in insulin pumps.

Authors:  Fabian León-Vargas; Remei Calm; Jorge Bondia; Josep Vehí
Journal:  J Diabetes Sci Technol       Date:  2012-11-01

3.  Interval Safety Layer Coupled With an Impulsive MPC for Artificial Pancreas to Handle Intrapatient Variability.

Authors:  María F Villa-Tamayo; Maira García-Jaramillo; Fabian León-Vargas; Pablo S Rivadeneira
Journal:  Front Endocrinol (Lausanne)       Date:  2022-02-21       Impact factor: 5.555

4.  Non-invasive continuous glucose monitoring with multi-sensor systems: a Monte Carlo-based methodology for assessing calibration robustness.

Authors:  Mattia Zanon; Giovanni Sparacino; Andrea Facchinetti; Mark S Talary; Martin Mueller; Andreas Caduff; Claudio Cobelli
Journal:  Sensors (Basel)       Date:  2013-06-03       Impact factor: 3.576

  4 in total

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