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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

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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 ▲]

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See detailModel-Based Recognition Using Persistent Scatterers
Dudgeon, Dan E.; Verly, Jacques ULg

Scientific conference (1993)

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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)

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See detailModel-based sensor of hemodynamics in critical care
Hann, C. E.; Starfinger, C.; Chase, J. G. et al

in ICST 2007 (2007)

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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 ▲]

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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: 26 (2 ULg)
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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

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See detailA Model-Based System for Automatic Target Recognition
Verly, Jacques ULg; Delanoy, Richard L.; Dudgeon, Dan E.

Conference (1991, April)

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See detailA Model-Based Target Recognition System
Dudgeon, Dan E.; Delanoy, Richard L.; Verly, Jacques ULg

Scientific conference (1990)

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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)

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See detailModel-based verification of a security protocol for conditional access to services
Leduc, Guy ULg; Bonaventure, Olivier; Koerner, Eckhart et al

in Formal Methods In System Design (1999), 14(2), 171-191

We use the formal language LOTOS to specify and verify the robustness of the Equicrypt protocol under design in the European OKAPI project for conditional access to multimedia services. We state some ... [more ▼]

We use the formal language LOTOS to specify and verify the robustness of the Equicrypt protocol under design in the European OKAPI project for conditional access to multimedia services. We state some desired security properties and formalize them. We describe a generic intruder process and its modelling, and show that some properties are falsified in the presence of this intruder. The diagnostic sequences can be used almost directly to exhibit the scenarios of possible attacks on the protocol. Finally, we propose an improvement of the protocol which satisfies our properties. [less ▲]

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See detailModel-based, Automatic Recognition of Tactical Vehicles in Laser-Radar Imagery: Principles and Evaluation
Verly, Jacques ULg; Delanoy, Richard L.; Dudgeon, Dan E.

Conference (1989, November 02)

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See detailModel-free Monte Carlo-like policy evaluation
Fonteneau, Raphaël ULg; Murphy, Susan A.; Wehenkel, Louis ULg et al

in 29th Benelux Meeting on Systems and Control (2010)

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See detailModel-free Monte Carlo-like policy evaluation
Ernst, Damien ULg

Speech/Talk (2010)

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See detailModel-free Monte Carlo-like policy evaluation
Fonteneau, Raphaël ULg; Murphy, Susan; Wehenkel, Louis ULg et al

in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010) (2010, May)

We propose an algorithm for estimating the finite-horizon expected return of a closed loop control policy from an a priori given (off-policy) sample of one-step transitions. It averages cumulated rewards ... [more ▼]

We propose an algorithm for estimating the finite-horizon expected return of a closed loop control policy from an a priori given (off-policy) sample of one-step transitions. It averages cumulated rewards along a set of “broken trajectories” made of one-step transitions selected from the sample on the basis of the control policy. Under some Lipschitz continuity assumptions on the system dynamics, reward function and control policy, we provide bounds on the bias and variance of the estimator that depend only on the Lipschitz constants, on the number of broken trajectories used in the estimator, and on the sparsity of the sample of one-step transitions. [less ▲]

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See detailModel-free Monte Carlo–like policy evaluation
Fonteneau, Raphaël ULg; Murphy, Susan; Wehenkel, Louis ULg et al

in Proceedings of Conférence Francophone sur l'Apprentissage Automatique (CAp) 2010 (2010, May)

We propose an algorithm for estimating the finite-horizon expected return of a closed loop control policy from an a priori given (off-policy) sample of one-step transitions. It averages cumulated rewards ... [more ▼]

We propose an algorithm for estimating the finite-horizon expected return of a closed loop control policy from an a priori given (off-policy) sample of one-step transitions. It averages cumulated rewards along a set of “broken trajectories” made of one-step transitions selected from the sample on the basis of the control policy. Under some Lipschitz continuity assumptions on the system dynamics, reward function and control policy, we provide bounds on the bias and variance of the estimator that depend only on the Lipschitz constants, on the number of broken trajectories used in the estimator, and on the sparsity of the sample of one-step transitions. [less ▲]

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See detailModel-free probability distance clustering of time series
Frasso, Gianluca ULg; D'Ambrosio, Antonio; Siciliano, Roberta

Conference (2013, December)

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See detailModel-guided bone tissue engineering: from bench to bedside via in silico modeling
Geris, Liesbet ULg

Conference (2014, September)

The creation of man-made living implants is the holy grail of tissue engineering (TE). As basic science advances, one of the major challenges in TE is the translation of the increasing biological ... [more ▼]

The creation of man-made living implants is the holy grail of tissue engineering (TE). As basic science advances, one of the major challenges in TE is the translation of the increasing biological knowledge on complex cell and tissue behavior into a predictive and robust engineering process. Mastering this complexity is an essential step towards clinical applications of TE. Computational modeling allows to study the biological complexity in a more integrative and quantitative way. Specifically, computational tools can help in quantifying and optimizing the TE product and process but also in assessing the influence of the in vivo environment on the behavior of the TE product after implantation. In this talk, I will use the example of bone tissue engineering to demonstrate how computational modeling can contribute in all aspects of the TE product development cycle: cells, carriers, culture conditions and clinics (figure 1 and 2). Depending on the specific question that needs to be answered the optimal model systems can vary from single scale to multiscale. Furthermore, depending on the available information, model systems can be purely data-driven or more hypothesis-driven in nature. The talk makes the case for in silico models receiving proper recognition, besides the in vitro and in vivo work in the TE field. Figure 1: overview of the 4 important components in bone tissue engineering: cells, carriers, culture and clinics. Figure 2: overview of in silico contributions to the 4 important components in bone tissue engineering: cells [1], carriers, culture [3] and clinics [4]. Acknowledgements This work presented in this talk is part of Prometheus, the KU Leuven R&D division for skeletal tissue engineering. http://www.kuleuven.be/prometheus. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreements 279100; from the Research Programme of the Research Foundation - Flanders (FWO, grant n. G.0982.11), from the Belgian National Fund for Scientific Research (FNRS) and from the special research fund of the KU Leuven (GOA/13/016) References 1. Kerkhofs J, Roberts SJ, Luyten FP, Van Oosterwyck H, Geris L. Relating the chondrocyte gene network to growth plate morphology: from genes to phenotype. PLoS One. 2012;7(4):e34729. doi: 10.1371/journal.pone.0034729 2. Guyot Y, Papantoniou I, Chai YC, Van Bael S, Schrooten J, Geris L. A computational model for cell/ECM growth on 3D surfaces using the level set method: a bone tissue engineering case study.Biomech Model Mechanobiol. 2014 3. Carlier A, Geris L, Bentley K, Carmeliet G, Carmeliet P, Van Oosterwyck H. MOSAIC: a multiscale model of osteogenesis and sprouting angiogenesis with lateral inhibition of endothelial cells. PLoS Comput Biol. 2012;8(10):e1002724. [less ▲]

Detailed reference viewed: 27 (0 ULg)