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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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See detailModel-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit
Sundaresan, Ashwath; Chase, J Geoffrey; Shaw, Geoffrey M et al

in Biomedical Engineering Online (2011), 10

Background: The optimal level of positive end-expiratory pressure (PEEP) is still widely debated in treating acute respiratory distress syndrome (ARDS) patients. Current methods of selecting PEEP only ... [more ▼]

Background: The optimal level of positive end-expiratory pressure (PEEP) is still widely debated in treating acute respiratory distress syndrome (ARDS) patients. Current methods of selecting PEEP only provide a range of values and do not provide unique patient-specific solutions. Model-based methods offer a novel way of using non-invasive pressure-volume (PV) measurements to estimate patient recruitability. This paper examines the clinical viability of such models in pilot clinical trials to assist therapy, optimise patient-specific PEEP, assess the disease state and response over time. Methods: Ten patients with acute lung injury or ARDS underwent incremental PEEP recruitment manoeuvres. PV data was measured at increments of 5 cmH(2)O and fitted to the recruitment model. Inspiratory and expiratory breath holds were performed to measure airway resistance and auto-PEEP. Three model-based metrics are used to optimise PEEP based on opening pressures, closing pressures and net recruitment. ARDS status was assessed by model parameters capturing recruitment and compliance. Results: Median model fitting error across all patients for inflation and deflation was 2.8% and 1.02% respectively with all patients experiencing auto-PEEP. In all three metrics' cases, model-based optimal PEEP was higher than clinically selected PEEP. Two patients underwent multiple recruitment manoeuvres over time and model metrics reflected and tracked the state or their ARDS. Conclusions: For ARDS patients, the model-based method presented in this paper provides a unique, non-invasive method to select optimal patient-specific PEEP. In addition, the model has the capability to assess disease state over time using these same models and methods. [less ▲]

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See detailModel-based PEEP optimisation in mechanical ventilation
Chiew, Y. S.; Chase, J. G.; Shaw, G. M. et al

in BioMedical Engineering OnLine (2011), 10

Background: Acute Respiratory Distress Syndrome (ARDS) patients require mechanical ventilation (MV) for breathing support. Patient-specific PEEP is encouraged for treating different patients but there is ... [more ▼]

Background: Acute Respiratory Distress Syndrome (ARDS) patients require mechanical ventilation (MV) for breathing support. Patient-specific PEEP is encouraged for treating different patients but there is no well established method in optimal PEEP selection.Methods: A study of 10 patients diagnosed with ALI/ARDS whom underwent recruitment manoeuvre is carried out. Airway pressure and flow data are used to identify patient-specific constant lung elastance (E <br /> lung) and time-variant dynamic lung elastance (E <br /> drs) at each PEEP level (increments of 5cmH <br /> 2O), for a single compartment linear lung model using integral-based methods. Optimal PEEP is estimated using E <br /> lungversus PEEP, E <br /> drs-Pressure curve and E <br /> drsArea at minimum elastance (maximum compliance) and the inflection of the curves (diminishing return). Results are compared to clinically selected PEEP values. The trials and use of the data were approved by the New Zealand South Island Regional Ethics Committee.Results: Median absolute percentage fitting error to the data when estimating time-variant E <br /> drsis 0.9% (IQR = 0.5-2.4) and 5.6% [IQR: 1.8-11.3] when estimating constant E <br /> lung. Both E <br /> lungand E <br /> drsdecrease with PEEP to a minimum, before rising, and indicating potential over-inflation. Median E <br /> drsover all patients across all PEEP values was 32.2 cmH <br /> 2O/l [IQR: 26.1-46.6], reflecting the heterogeneity of ALI/ARDS patients, and their response to PEEP, that complicates standard approaches to PEEP selection. All E <br /> drs-Pressure curves have a clear inflection point before minimum E <br /> drs, making PEEP selection straightforward. Model-based selected PEEP using the proposed metrics were higher than clinically selected values in 7/10 cases.Conclusion: Continuous monitoring of the patient-specific E <br /> lungand E <br /> drsand minimally invasive PEEP titration provide a unique, patient-specific and physiologically relevant metric to optimize PEEP selection with minimal disruption of MV therapy. © 2011 Chiew et al; licensee BioMed Central Ltd. [less ▲]

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See detailModel-based prediction of the patient-specific response to adrenaline
Chase, J. G.; Starfinger, C.; Hann, C. E. et al

in The open medical informatics journal (2010), 4

Detailed reference viewed: 17 (0 ULg)
See detailModel-Based Recognition Using Persistent Scatterers
Dudgeon, Dan E.; Verly, Jacques ULg

Scientific conference (1993)

Detailed reference viewed: 6 (1 ULg)