1,721,029 research outputs found
Optimal Design of Experiments Based on Artificial Neural Network Classifiers for Fast Kinetic Model Recognition
No full textDeveloping mathematical models for the description of reaction kinetics is fundamental for process design, control and optimisation. The problem of model discrimination among a set of candidate models is not trivial, and recently a new and complementary approach based on artificial neural networks (ANNs) for kinetic model recognition was proposed. This paper extends the ANNs-based model identification approach by defining an optimal design of experiment procedure, whose performance is assessed through a simulated case study. The proposed design of experiments method allows to reduce the number of experiments to be conducted while increasing the ability of the artificial neural network in recognising the proper kinetic model structure
A Disturbance Estimation Approach for Online Model-based Redesign of Experiments in the Presence of Systematic Errors
No full textOnline Model-Based Redesign of Experiment (OMBRE) strategies represent a valuable support to the development of dynamic deterministic models, allowing for the dynamic update of the experimental conditions to yield the most informative data for the parameter identification task. However, the effectiveness of OMBRE strategies may be severely affected by the presence of systematic modelling errors. In this paper, a disturbance estimation approach is exploited within an OMBRE framework (DEOMBRE) in order to achieve a statistically satisfactory estimation of the model parameters, thus avoiding (or reducing) constraint violations even in the presence of systematic modelling errors. A case study illustrates the benefits of the new approach. © 2011 Elsevier B.V
Identification of complex models of type 2 diabetes from IVGTT data by model-based design of experiments
No full textIntravenous glucose tolerance tests (IVGTTs) are typically used to assess insulin resistance and insulin secretion activities in subjects affected by type 2 diabetes by adopting minimal models. However, the amount of information that can be obtained from IVGTTs for the purpose of model identification is intrinsically related to the dynamics triggered by the intravenous glucose infusion and to the individual specificity. This paper shows how the information content of clinical data from conventional IVGTTs can be handled by model-based design of experiments (MBDoE) techniques when the goal is to estimate the set of parameters of a complex model of type 2 diabetes. MBDoE allows to analyse and improve the information content of IVGTTs by optimising the sample allocation in such a way as to decrease the degree of correlation between critical parameters. © 2013 Elsevier B.V
An optimal experimental design framework for fast kinetic model identification based on artificial neural networks
Full text linkThe development of mathematical models to describe reaction kinetics is crucial in process design, control, and optimisation. However, distinguishing between different candidate kinetic models presents a non-trivial challenge. Recent works on this topic introduced an approach that employs artificial neural networks (ANNs) to identify kinetic models. In this paper, the ANNs-based model identification approach is expanded by introducing an optimal experimental design procedure. The performance of the method is evaluated through a case study
related to the identification of kinetics in a batch reaction system, where different combinations of experimental design variables and noise level on the measurements are compared to assess their impact on kinetic model identification. The proposed experimental design methodology effectively reduces the number of required experiments while enhancing the artificial neural network’s ability to accurately identify the appropriate set of equations defining the kinetic model structure
A framework for the optimal design of a minimum set of clinical trials to characterize von Willebrand disease
No full textOnline model-based redesign of experiments with erratic models: A disturbance estimation approach
No full textModel-based design of experiment (MBDoE) techniques are a useful tool to maximise the information content of experimental trials when the purpose is identifying the set of parameters of a deterministic model in a statistically sound way. In a conventional MBDoE procedure, the information gathered during the evolution of an experiment is exploited only at the end of the experiment itself. Conversely, online model-based redesign of experiment (OMBRE) techniques have been recently proposed to exploit the information as soon as it is generated by the running experiment, allowing for the dynamic update of the experimental conditions to yield the most informative data in order to improve the parameter identification task. However, the effectiveness of MBDoE strategies (including OMBRE) may be severely affected by the presence of systematic modelling errors as well as by disturbances acting on the system. In this paper, a novel experiment design approach (DE-OMBRE) is presented, where a model updating policy including disturbance estimation (DE) is embedded within an OMBRE strategy in order to achieve a statistically satisfactory estimation of the model parameters as well as to estimate the possible discrepancy between the real system and the model being identified. The procedure allows reducing (or even avoiding) constraint violations, preserving the optimality of the redesign even in the presence of systematic errors and/or unknown disturbances acting on the system. Two simulated case studies of different levels of complexity are used to illustrate the benefits of the novel approach. (c) 2011 Elsevier Ltd. All rights reserved
Von Willebrand disease type Vicenza: In search of a classification for the archetype of reduced von Willebrand factor survival
No full textType Vicenza von Willebrand disease (VWD) features a von Willebrand factor (VWF) with a very short half-life, and is classified as a form of type 1 VWD. To test the appropriateness of type Vicenza VWD classification, the main features of 17 patients from eight unrelated families were analysed. They had low VWF antigen levels and function (always below 20 U/dl); ristocetin-induced platelet aggregation sometimes normal, sometimes reduced/absent (even in the same patient); normal platelet VWF levels; an increased VWF propeptide to VWF antigen ratio (8.74 ± 1.65 vs. normal 1.04 ± 0.28) and a reduced VWF half-life. Plasma VWF multimer levels were homogeneously reduced, and unusually large VWF multimers were sometimes present. Recombinant p.R1205H VWF showed a normal synthesis, release, function, and multimer pattern, with no ultra-large VWF multimers. The mathematical model by Galvanin et al. was used to explore the kinetic changes in VWF after DDAVP. It showed that the release, but especially the proteolysis (k proteol 1.0-3 ± 2.5-3 vs. normal 4.5-4 ± 6.4-4) and elimination (k el 1.0-2 ± 5.2-3 vs. normal 1.1-3 ± 6.8-4) of type Vicenza VWF were significantly higher than normal. The increased elimination is consistent with the short half-life, while the increased proteolysis was unexpected. As a shorter survival of VWF is wholly responsible for the type Vicenza VWD phenotype (VWF synthesis, structure and function are normal), it might be better to classify it as a type 2 VWD (rather than type 1) to emphasise the greater interaction with clearance receptors as a new VWF functional defect
Towards the optimal design of a minimal set of clinical tests for the identification and characterization of von Willebrand disease
No full textAn exploratory model-based design of experiments approach to aid parameters identification and reduce model prediction uncertainty
Full text linkThe management of trade-off between experimental design space exploration and information maximization is still an open question in the field of optimal experimental design. In classical optimal experimental design methods, the uncertainty of model prediction throughout the design space is not always assessed after parameter identification and parameters precision maximization do not guarantee that the model prediction variance is minimized in the whole domain of model utilization. To tackle these issues, we propose a novel model-based design of experiments (MBDoE) method that enhances space exploration and reduces model prediction uncer-tainty by using a mapping of model prediction variance (G-optimality mapping). This explorative MBDoE (eMBDoE) named G-map eMBDoE is tested on two models of increasing complexity and compared against con-ventional factorial design of experiments, Latin Hypercube (LH) sampling and MBDoE methods. The results show that G-map eMBDoE is more efficient in exploring the experimental design space when compared to a standard MBDoE and outperforms classical design of experiments methods in terms of model prediction uncertainty reduction and parameters precision maximization
A framework for the optimal design of a minimum set of clinical trials to characterize von Willebrand disease
Full text linkBackground and Objective: Von Willebrand disease (VWD) is one of the most severe inherited bleeding disorder in humans, and it is associated with a qualitative and/or quantitative deficiency of von Willebrand factor, a multimeric glycoprotein fundamental in the coagulation process. At present, the diagnosis of VWD is extremely challenging and mostly based on clinical experience. Kinetic models have been recently proposed and applied to help in the diagnosis and characterization of VWD, but the complexity of these models is such that they requires long and stressful clinical tests, such as the desmopressin response test (DDAVP), to achieve a satisfactory estimation of the individual haemostatic parameters. The goal of this paper is to design a minimal set of clinical tests for the identification of akinetic model to decrease the required time and effort for the characterization and diagnosis of VWD. Methods: A model proposed in the literature is used as a building block to develop a new model, where response surface methodologies have been applied to determine a set of explicit correlations linkingkinetic model parameters to basal clinical trials data. Model-based design of experiments techniques are then used to devise optimally informative tests for model validation which are shorter and easier to implement. Results: Results show an excellent agreement between the original model for VWD and the new proposed model on representing healthy and VWD subjects. The application of experimental design techniques for model validation shows the possibility to drastically reduce the duration of DDAVP tests from 24 h–3 h by exploiting complementary information from basal clinical tests. Conclusions: Basal clinical tests can be used alongside a time-reduced DDAVP test to validate pharmacokinetic models for a quantitative characterisation of subjects affected by VWD and for a quicker and easier diagnosis of the disease
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