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Estimation for dynamical systems using a population-based Kalman filter - Applications to pharmacokinetics models

dc.rights.licenseopenen_US
dc.contributor.authorCOLLIN, Annabelle
IDREF: 181605619
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPRAGUE, Melanie
dc.contributor.authorMOIREAU, Philippe
dc.date.accessioned2021-05-07T09:24:52Z
dc.date.available2021-05-07T09:24:52Z
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/27192
dc.description.abstractEnMany methods exist to identify parameters of dynamical systems. Unfortunately, in addition to the classical measurement noise and under-sampling drawbacks, mean and variance priors of the estimated parameters can be very vague. These difficulties can lead the estimation procedure to underfitting. In clinical studies, a circumvention consists in using the fact that multiple independent patients are observed as proposed by nonlinear mixed-effect models. However, these very effective approaches can turn to be time-consuming or even intractable when the model complexity increases. Here, we propose an alternative strategy of controlled complexity. We first formulate a population least square estimator and its associated a Kalman based filter, hence defining a robust large population sequential estimator. Then, to reduce and control the computational complexity, we propose a reduced-order version of this population Kalman filter based on a clustering technique applied to the observations. Using simulated pharmacokinetics data and the theophylline pharmacokinetics data, we compare the proposed approach with literature methods. We show that using the population filter improves the estimation performance compared to the classical and fast patient-by-patient Kalman filter and leads to estimation results comparable to state-of-the-art population-based approaches. Then, the reduced-order version allows to drastically reduce the computational time for equivalent estimation and prediction.
dc.language.isoENen_US
dc.title.enEstimation for dynamical systems using a population-based Kalman filter - Applications to pharmacokinetics models
dc.typeDocument de travail - Pré-publicationen_US
dc.subject.halMathématiques [math]/Statistiques [math.ST]en_US
dc.subject.halMathématiques [math]/Optimisation et contrôle [math.OC]en_US
dc.subject.halInformatique [cs]/Modélisation et simulationen_US
dc.subject.halSciences du Vivant [q-bio]/Sciences pharmaceutiques/Pharmacologieen_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamSISTM_BPH
bordeaux.import.sourcehal
hal.identifierhal-02869347
hal.version1
hal.exportfalse
workflow.import.sourcehal
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=COLLIN,%20Annabelle&PRAGUE,%20Melanie&MOIREAU,%20Philippe&rft.genre=preprint


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