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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

Detailed reference viewed: 113 (57 ULg)
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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)

Detailed reference viewed: 33 (1 ULg)
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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 ▲]

Detailed reference viewed: 72 (21 ULg)
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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 ▲]

Detailed reference viewed: 37 (9 ULg)
See detailA model-based optimization approach for kinetic parameter estimation in reactive extraction
Bertakis, Evangelos; Kalem, Murat; Pfennig, Andreas ULg et al

Conference (2007)

Detailed reference viewed: 3 (2 ULg)
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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 ▲]

Detailed reference viewed: 130 (12 ULg)
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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

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See detailModel-Based Prediction of the Response to Vascular Filling Therapy
Pironet, Antoine ULg

Doctoral thesis (2016)

Vascular filling is one of the most frequent interventions in intensive care units. Its expected effect is to increase cardiac output. However, this increase is only observed in approximately 50 % of ... [more ▼]

Vascular filling is one of the most frequent interventions in intensive care units. Its expected effect is to increase cardiac output. However, this increase is only observed in approximately 50 % of cases. In addition, excessive vascular filling can lead to deleterious effects, such as pulmonary oedema, which increase length of ventilation, stay, mortality and cost. Clinicians are thus looking for indices to provide a priori knowledge of the effect of vascular filling. This thesis focuses on a mathematical model-based approach to predict the response to vascular filling. Mathematical models are sets of equations representing the behaviour of a given system as, for instance, the cardiovascular system. To understand the concept of vascular filling, basic elements of cardio-vascular anatomy and physiology are presented in the first part of this thesis. Then, fur- ther details about vascular filling therapy are given, as well as the current indices used by clinicians to predict its effects. The static indices are easy to obtain, but do not perform well. The dynamic indices, based on cardio-pulmonary interac- tions, perform better, but are difficult and highly invasive to implement clinically. A new index, total stressed blood volume, also seems to perform well, but is not easy to obtain clinically. This work develops and then uses models of the cardio- vascular system to make this parameter available to clinicians. Building on the elements of physiology provided in the first part, the second part of this thesis describes ways to model the components of the cardio-vascular system as lumped elements, such as chambers, valves and resistances. Two mod- els of the cardio-vascular system, comprising respectively three and six cham- bers, are built from such elements. These two models involve a small number of parameters, including the total stressed volume in the model. The third part of this thesis describes the potential and methods to identify the parameters of the two cardio-vascular system models. Parameter identifica- tion aims at finding the parameter values that make model simulations as close as possible to measured data. The available data is thus first described, accord- ing to whether it is collected in an experimental laboratory or an intensive care unit. Then, it is mathematically demonstrated that all model parameters can the- oretically be identified from data available in an intensive care unit. However, practically speaking, some parameters are difficult to identify, because they have little influence on the simulations, or have the same effect as other parameters. Fi- nally, computational methods to perform parameter identification are presented and compared. The last part of this thesis presents two applications of the cardio-vascular system models to experimental data. First, all parameters of the six-chamber cardio-vascular system model are identified from data recorded during a preload reduction experiment. This result provides the first quantitative validation of the six-chamber model in transient conditions. Second, all parameters of the three-chamber cardio-vascular system model, including total stressed volume, are identified from data recorded during vascular filling experiments. The total stressed volume parameter is shown to be systematically related to the change in cardiac output after vascular filling. This last index thus provides, for the first time, a model-based means of predicting the response to vascular filling. [less ▲]

Detailed reference viewed: 62 (18 ULg)
See detailModel-Based Recognition Using Persistent Scatterers
Dudgeon, Dan E.; Verly, Jacques ULg

Scientific conference (1993)

Detailed reference viewed: 8 (1 ULg)
Peer Reviewed
See detailA model-based relevance estimation approach for feature selection in microarray datasets
Bontempi, Gianluca; Meyer, Patrick ULg

in Artificial Neural Networks-ICANN 2008 (2008)

Detailed reference viewed: 20 (4 ULg)
Peer Reviewed
See detailModel-based sensor of hemodynamics in critical care
Hann, C. E.; Starfinger, C.; Chase, J. G. et al

in ICST 2007 (2007)

Detailed reference viewed: 8 (0 ULg)
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See detailModel-Based Stressed Blood Volume is an Index of Fluid Responsiveness
Pironet, Antoine ULg; Dauby, Pierre ULg; Chase, J. Geoffrey et al

in IFAC PapersOnLine (2015, September)

Fluid therapy is frequently used to manage acute circulatory failure. This therapy aims to restore cardiac output by fluid administration, which increases the quantity of fluid in the circulation. However ... [more ▼]

Fluid therapy is frequently used to manage acute circulatory failure. This therapy aims to restore cardiac output by fluid administration, which increases the quantity of fluid in the circulation. However, it has been shown to be effective only in certain cases, leading to the need for indices of fluid responsiveness. Total stressed blood volume has recently been shown to be such an index of fluid responsiveness. However, the current methods to determine this parameter require specific procedures. In this work, a more straightforward method is developed using data available in the intensive care unit. A simple three-chamber cardiovascular system model is used, of which total stressed blood volume is a parameter. All model parameters (including total stressed blood volume) are adjusted to pig experimental data during fluid administrations. The resulting value of total stressed blood volume is always negatively associated with the relative change in cardiac output after fluid administration. This finding confirms that total stressed blood volume is an index of fluid responsiveness. Another finding of this study is that the response curves are subject-specific. The method developed in this work can be applied to humans, since the data required is typically available in an intensive care unit. [less ▲]

Detailed reference viewed: 30 (4 ULg)
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See detailModel-Based Stressed Blood Volume is an Index of Fluid Responsiveness
Pironet, Antoine ULg; Dauby, Pierre ULg; Chase, J. Geoffrey et al

Conference (2015, September 01)

Fluid therapy is frequently used to manage acute circulatory failure. This therapy aims to restore cardiac output by fluid administration, which increases the quantity of fluid in the circulation. However ... [more ▼]

Fluid therapy is frequently used to manage acute circulatory failure. This therapy aims to restore cardiac output by fluid administration, which increases the quantity of fluid in the circulation. However, it has been shown to be effective only in certain cases, leading to the need for indices of fluid responsiveness. Total stressed blood volume has recently been shown to be such an index of fluid responsiveness. However, the current methods to determine this parameter require specific procedures. In this work, a more straightforward method is developed using data available in the intensive care unit. A simple three-chamber cardiovascular system model is used, of which total stressed blood volume is a parameter. All model parameters (including total stressed blood volume) are adjusted to pig experimental data during fluid administrations. The resulting value of total stressed blood volume is always negatively associated with the relative change in cardiac output after fluid administration. This finding confirms that total stressed blood volume is an index of fluid responsiveness. Another finding of this study is that the response curves are subject-specific. The method developed in this work can be applied to humans, since the data required is typically available in an intensive care unit. [less ▲]

Detailed reference viewed: 28 (3 ULg)
Peer Reviewed
See detailModel-Based System for Automatic Target Recog- nition from Forward-Looking Laser-Radar Imagery
Verly, Jacques ULg; Delanoy, Richard L.; Dudgeon, Dan E.

in Optical Engineering : The Journal of the Society of Photo-Optical Instrumentation Engineers (1992), 31(12), 2540-2552

Detailed reference viewed: 8 (0 ULg)
Peer Reviewed
See detailA Model-Based System for Automatic Target Recognition
Verly, Jacques ULg; Delanoy, Richard L.; Dudgeon, Dan E.

Conference (1991, April)

Detailed reference viewed: 8 (0 ULg)
See detailA Model-Based Target Recognition System
Dudgeon, Dan E.; Delanoy, Richard L.; Verly, Jacques ULg

Scientific conference (1990)

Detailed reference viewed: 6 (0 ULg)
Peer Reviewed
See detailModel-based therapeutics for the cardiovascular system - a clinical focus
Hann, C. E.; Chase, J. G.; Desaive, Thomas ULg et al

in 6th IFAC Symposium on Modeling and Control in Biomedical Systems (MCBMS09) (2009)

Detailed reference viewed: 14 (4 ULg)