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Peer Reviewed
See detailA Model-Based Automatic Target Recognition System for Synthetic Aperture Radar Imagery
Verly, Jacques ULg; Delanoy, Richard L.; Lazott, Carol H. et al

Conference (1992, March)

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See detailModel-based cardiac diagnosis of pulmonary embolism
Starfinger, C.; Hann, C. E.; Chase, J. G. et al

in Computer Methods & Programs in Biomedicine (2007), 87(1), 46-60

A minimal cardiac model has been shown to accurately capture a wide range of cardiovascular system dynamics commonly seen in the intensive care unit (ICU). However, standard parameter identification ... [more ▼]

A minimal cardiac model has been shown to accurately capture a wide range of cardiovascular system dynamics commonly seen in the intensive care unit (ICU). However, standard parameter identification methods for this model are highly non-linear and non-convex, hindering real-time clinical application. An integral-based identification method that transforms the problem into a linear, convex problem, has been previously developed, but was only applied on continuous simulated data with random noise. This paper extends the method to handle discrete sets of clinical data, unmodelled dynamics, a significantly reduced data set theta requires only the minimum and maximum values of the pressure in the aorta, pulmonary artery and the volumes in the ventricles. The importance of integrals in the formulation for noise reduction is illustrated by demonstrating instability in the identification using simple derivative-based approaches. The cardiovascular system (CVS) model and parameter identification method are then clinically validated on porcine data for pulmonary embolism. Errors for the identified model are within 10% when re-simulated and compared to clinical data. All identified parameter trends match clinically expected changes. This work represents the first clinical validation of these models, methods and approach to cardiovascular diagnosis in critical care. (c) 2007 Elsevier Ireland Ltd. All rights reserved. [less ▲]

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See detailModel-based cardiac disease diagnosis in critical care
Revie, JA; Hann, CE; Stevenson, D et al

in Proceedings of the Health Research Society of Canterbury (HRSC) Clinical Meeting 2010 (2010)

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See detailModel-based cardiovascular monitoring of acute pulmonary embolism in porcine trials
Revie, JA; Stevenson, DJ; Chase, JG et al

in Critical Care (2011), 15 (Suppl 1)

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See detailModel-based cardiovascular monitoring of large pore hemofiltration during endotoxic shock in pigs
Revie, JA; Stevenson, DJ; Chase, JG et al

in Critical Care (2011), 15 (Suppl 1)

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See detailModel-based Cardiovascular Therapeutics: Capturing the patient-specific impact of inotrope therapy
Desaive, Thomas ULg; Starfinger, C; Chase, JG et al

in Proceedings of the 3rd International Meeting of the French Society of Hypertension (2009)

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See detailModel-based Design and Verification of Security Protocols using LOTOS
Germeau, François; Leduc, Guy ULg

in Design and Formal Verification of Security Protocols (1997, September)

We explain how the formal language LOTOS can be used to specify security protocols and cryptographic operations. We describe how to model security properties as safety properties and how a model-based ... [more ▼]

We explain how the formal language LOTOS can be used to specify security protocols and cryptographic operations. We describe how to model security properties as safety properties and how a model-based verification method can be used to verify the robustness of a protocol against attacks of an intruder. We illustrate our technique on a concrete registration protocol. We find a simpler protocol that remains secure, and a more sophisticated protocol that allows a better distinction between intruder's attacks and ordinary errors. [less ▲]

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See detailModel-based detection of pulmonary embolism using an extended physiologically relevant, cardiovascular model
Kok, K.; Starfinger, C.; Hann, C. E. et al

in Proceedings of Engineering & Physical Sciences in Medicine and Australian Biomedical Engineering Conference (EPSM ABEC 2008) (2008)

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See detailModel-based diagnosis of acute pulmonary embolism - results from a porcine model
Desaive, Thomas ULg; Ghuysen, Alexandre ULg; Kolh, Philippe ULg et al

in Intensive Care Medicine (2008), 34(suppl. 1), 78

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See detailModel-based diagnosis of acute pulmonary embolism and septic shock in porcine trials
Revie, JA; Stevenson, D; Chase, JG et al

in Proceedings of the Health Research Society of Christchurch Annual Scientific Session 2011 (2011)

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See detailModel-based Experimental Target Recognition System
Dudgeon, Dan E.; Delanoy, Richard L.; Verly, Jacques ULg

Scientific conference (1989, May)

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See detailModel-based glycemic control in critical care
Pretty, Christopher ULg; Penning, Sophie ULg; Le Compte, Aaron J. et al

Poster (2012, December)

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See detailModel-based glycemic control in critical care
Pretty, Christopher ULg; Penning, Sophie ULg; Le Compte, Aaron J. et al

in Proceedings of the 11th Belgian Day on Biomedical Engineering (2012, December)

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See detailModel-based identification and diagnosis of a porcine model of induced endotoxic shock with hemofiltration
Starfinger, C.; Chase, J. G.; Hann, C. E. et al

in Mathematical Biosciences (2008), 216(2), 132-139

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See detailModel-Based Ladar ATR Using Functional Templates
Verly, Jacques ULg

Conference (1996, September)

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See detailModel-based Monitoring of Septic Shock Treated with Large-Pore Hemofiltration Therapy
Revie; Stevenson, D; Chase, JG et al

in Proceedings of BMS 2012 (2012)

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See detailModel-Based Multifactor Dimensionality Reduction for detecting epistasis in case-control data in the presence of noise
Cattaert, Tom ULg; Calle, Luz M; Dudek, Scott T et al

in Annals of Human Genetics (2011), 75(1), 78-89

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See detailModel-Based Multifactor Dimensionality Reduction for detecting interactions in high-dimensional genomic data.
Calle, M L; Urrea, V; Vellalta, G et al

Report (2008)

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See detailModel-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data.
Mahachie John, Jestinah ULg; Van Lishout, François ULg; Van Steen, Kristel ULg

in European Journal of Human Genetics (2011), 19

Detecting gene-gene interactions or epistasis in studies of human complex diseases is a big challenge in the area of epidemiology. To address this problem, several methods have been developed, mainly in ... [more ▼]

Detecting gene-gene interactions or epistasis in studies of human complex diseases is a big challenge in the area of epidemiology. To address this problem, several methods have been developed, mainly in the context of data dimensionality reduction. One of these methods, Model-Based Multifactor Dimensionality Reduction, has so far mainly been applied to case-control studies. In this study, we evaluate the power of Model-Based Multifactor Dimensionality Reduction for quantitative traits to detect gene-gene interactions (epistasis) in the presence of error-free and noisy data. Considered sources of error are genotyping errors, missing genotypes, phenotypic mixtures and genetic heterogeneity. Our simulation study encompasses a variety of settings with varying minor allele frequencies and genetic variance for different epistasis models. On each simulated data, we have performed Model-Based Multifactor Dimensionality Reduction in two ways: with and without adjustment for main effects of (known) functional SNPs. In line with binary trait counterparts, our simulations show that the power is lowest in the presence of phenotypic mixtures or genetic heterogeneity compared to scenarios with missing genotypes or genotyping errors. In addition, empirical power estimates reduce even further with main effects corrections, but at the same time, false-positive percentages are reduced as well. In conclusion, phenotypic mixtures and genetic heterogeneity remain challenging for epistasis detection, and careful thought must be given to the way important lower-order effects are accounted for in the analysis.European Journal of Human Genetics advance online publication, 16 March 2011; doi:10.1038/ejhg.2011.17. [less ▲]

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