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作者： Leite, Valter J.S. Castelan, Eugênio B. Miranda, Márcio F. Viana, Dimitri C. CEFET-MG Campus Divinópolis Rua Monte Santo 319 35502-036 Divinópolis MG Brazil DAS-CTC/UFSC P.O. Box 476 88040-900 Florianópolis SC Brazil COLTEC UFMG Av. Antônio Carlos 6627 31270-010 Belo Horizonte MG Brazil Graduate Program on Mathematical and Computational Modelling CEFET-MG Av. Amazonas 5253 30480-000 Belo Horizonte MG Brazil

ISBN: (纸本)9781424474264

Convex conditions, proposed as a feasibility test of a set of linear matrix inequalities (LMIs), are given for the design of a partially parameter dependent dynamic output feedback controller for a class of discrete time systems with delayed state. This class concerns the systems with time-varying delay in the state and time varying parameters, both assumed to be available on line. The controller is composed by a classical part, feeding-back the system output and another one where the delayed output of the system is feedback. This last part can be avoided in case of inaccessible delay value, resulting in a more classical control design. Different from other approaches found in the literature, it is used a parameter dependent Lyapunov-Krasovskii functional candidate and a slack matrix variable to reduce the conservatism of the proposed approach. The resulting parameter dependent controller can be obtained on line as a convex combination of some 'vertex' controllers. Numerical examples are presented to illustrated the effectiveness of the proposal. © 2010 AACC.

关键词： Controllers

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Bayesian data assimilation to support informed decision-making in individualised chemotherapy

arXiv

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arXiv 2019年

作者： Maier, Corinna Hartung, Niklas de Wiljes, Jana Kloft, Charlotte Huisinga, Wilhelm Institute of Mathematics University of Potsdam Germany Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling Freie Universität Berlin University of Potsdam Germany Departement of Mathematics and Statistics University of Reading Whiteknights United Kingdom Department of Clinical Pharmacy and Biochemistry Institute of Pharmacy Freie Universität Berlin Germany

An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a-posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualised chemotherapy. Sequential Bayesian DA proved to be most computational efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas. Copyright © 2019, The Authors. All rights reserved.

关键词： Hospital data processing

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A continued learning approach for model-informed precision dosing: Updating models in clinical practice

arXiv

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arXiv 2021年

作者： Maier, Corinna de Wiljes, Jana Hartung, Niklas Kloft, Charlotte Huisinga, Wilhelm Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling Freie Universität Berlin University of Potsdam Germany Department of Clinical Pharmacy and Biochemistry Institute of Pharmacy Freie Universität Berlin Germany Institute of Mathematics University of Potsdam Karl-Liebknecht-Str. 24-25 Potsdam/Golm14476 Germany

Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/ biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to include also altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, since only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step towards building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use. © 2021, CC BY-NC-SA.

关键词： Inverse problems

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Fuzzy control of delayed systems: less conservative convex conditions

Fuzzy control of delayed systems: less conservative convex c...

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American Control Conference

作者： Dimitri C. Viana Valter J. S. Leite Marcio F. Miranda Graduate Program on Mathematical and Computational Modelling (GPMCM) CEFET-MG Av. Amazonas 5253 30480-000 Belo Horizonte MG Brazil CEFET-MG/Campus Divinopolis Rua Monte Santo 319 35502-036 Divinopolis MG Brazil COLTEC/UFMG Av. Antonio Carlos 6627 31270-010 Belo Horizonte MG Brazil

ISBN: (纸本)9781424474264

Convex conditions for stability analysis and control synthesis for discrete-time fuzzy systems with delayed state are proposed in this paper. The conditions are obtained by considering a parameter dependent Lyapunov-Krasovskii functional, slack matrix variables and, in the synthesis case, an instrumental variable. In all cases, the conditions are formulated as an LMI (linear matrix inequality) feasibility test, that can be efficiently solved in polynomial time by specialized numerical solvers. It is shown that the proposed conditions encompass quadratic stability based formulations. Moreover, in the stability analysis case, conditions published recently in the literature can be recovered as a particular case from those proposed in this paper. Convex conditions for controller synthesis are proposed using an instrumental variable, yielding complementary results when compared with other conditions recently published in the literature. Numerical examples are presented to illustrate the efficiency of the proposed conditions. Comparisons are made with other conditions from the literature.

关键词： Fuzzy control Delay systems Fuzzy systems Linear matrix inequalities Stability analysis Instruments Control systems Control system synthesis Nonlinear systems Digital control

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Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology

arXiv

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arXiv 2020年

作者： Maier, Corinna Hartung, Niklas Kloft, Charlotte Huisinga, Wilhelm de Wiljes, Jana Institute of Mathematics University of Potsdam Germany Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling Freie Universität Berlin University of Potsdam Germany Department of Clinical Pharmacy and Biochemistry Institute of Pharmacy Freie Universität Berlin Germany Institute of Mathematics Universität Potsdam Karl-Liebknecht-Str. 24-25 Potsdam/Golm14476 Germany

Model-informed precision dosing (MIPD) using therapeutic drug/biomarker monitoring offers the opportunity to significantly improve the efficacy and safety of drug therapies. Current strategies comprise model-informed dosing tables or are based on maximum a-posteriori estimates. These approaches, however, lack a quantification of uncertainty and/or consider only part of the available patient-specific information. We propose three novel approaches for MIPD employing Bayesian data assimilation (DA) and/or reinforcement learning (RL) to control neutropenia, the major dose-limiting side effect in anticancer chemotherapy. These approaches have the potential to substantially reduce the incidence of life-threatening grade 4 and subtherapeutic grade 0 neutropenia compared to existing approaches. We further show that RL allows to gain further insights by identifying patient factors that drive dose decisions. Due to its flexibility, the proposed combined DA-RL approach can easily be extended to integrate multiple endpoints or patient-reported outcomes, thereby promising important benefits for future personalized therapies. Copyright © 2020, The Authors. All rights reserved.

关键词： Chemotherapy

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Dynamic Output Stabilizing Design for Discrete-Time Fuzzy Systems With Time-Varying Delay

Dynamic Output Stabilizing Design for Discrete-Time Fuzzy Sy...

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2010 49th IEEE Conference on Decision and Control

作者： Dimitri C. Viana Valter J. S. Leite Eugenio B. Castelan Marcio F. Miranda Graduate Program on Mathematical and Computational Modelling (GPMCM) CEFET-MG Av. Amazonas 7576 30480-000 Belo Horizonte MG Brazil CEFET-MG / Campus Divinopolis R. Alvares Azevedo 400 35500-970 Divinopolis MG Brazil UFSC DAS-CTC Florianpolis SC 88040-900 BRAZIL COLTEC / UFMG Av. Antonio Carlos 6627 31270-010 Belo Horizonte MG Brazil

ISBN: (纸本)9781424477456

The design of a distributed-compensation (DC) output feedback controller for discrete-time fuzzy systems with time-varying delay is proposed by the formulation of convex optimization problems, formulated as a set of linear matrix inequalities (LMIs). At the exception of controller matrix D, all other are designed depending on the submodes of the fuzzy system and assembled together by fuzzy inference rules. The novelty of this proposal is that the controller is composed by a classical part, feeding-back the system output, and another where the delayed output of the system is dynamically feedback, whenever the delay is known. Different from other approaches found in the literature, it is used a parameter dependent Lyapunov-Krasovskii function candidate and extra matrices to reduce the conservatism of the proposed approach. Numerical experiments are presented showing the effectiveness of the proposal.

关键词： Fuzzy systems time varying output(systems) discrete fuzzy system discrete time systems Controllers discrete-time Conservatism Fuzzy inference rule Novelty delay

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Dynamic output compensator design for time-varying discrete time systems with delayed states

Dynamic output compensator design for time-varying discrete ...

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American Control Conference

作者： Valter J. S. Leite Eugenio B. Castelan Marcio F. Miranda Dimitri C. Viana CEFET-MG/Campus Divinopolis Rua Monte Santo 319 35502-036 Divinopolis MG Brazil DAS-CTC/UFSC P.O. Box 476 88040-900 Florianopolis SC Brazil COLTEC/UFMG Av. Antonio Carlos 6627 31270-010 Belo Horizonte MG Brazil Graduate Program on Mathematical and Computational Modelling CEFET-MG Av. Amazonas 5253 30480-000 Belo Horizonte MG Brazil

ISBN: (纸本)9781424474264

关键词： Time varying systems Discrete time systems Delay systems Delay effects Output feedback Control systems System testing Linear matrix inequalities Linear feedback control systems Control design

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Sample-based robust model reduction for non-linear systems biology models

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PAMM 2023年第4期23卷

作者： Undine Falkenhagen Christian Himpe Jane Knöchel Charlotte Kloft Wilhelm Huisinga Institute of Mathematics University of Potsdam Potsdam Germany Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling Freie Universität Berlin and University of Potsdam Potsdam Germany Mathematics Münster University of Münster Muenster Germany Department of Clinical Pharmacy and Biochemistry Institute of Pharmacy Freie Universität Berlin Berlin Germany

Complex non-linear systems biology models comprise relevant knowledge on processes of pharmacological interest. They are, however, too complex to be used in inferential settings, for example, to allow for the estimation of patient-specific parameters for individual dose optimisation. Thus, there is a need for simple models with interpretable components to infer the drug effect in a clinical setting. In particular, it is essential to accurately quantify and simulate the interindividual variability in the drug response in order to account for covariates like body weight, age and genetic disposition. To this end, non-linear model order reduction and simplification methods can be used if they maintain model interpretability during reduction and consider an entire population rather than just a single reference individual. We present a sample-based approach for robust model order reduction and propose two improvements for efficiency. In particular, we introduce a new sampling method to generate the virtual population based on transformed latin hypercube sampling. Thereby, the sample is stratified in the relevant parameter-space directions, which are identified using empirical observability Gramians. We illustrate our approach in application to a blood coagulation pathway model, where we reduce the complexity from a 62-dimensional highly non-linear to a six-dimensional and a nine-dimensional system of ordinary differential equations for two scenarios, respectively.

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26th Annual computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

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The Lancet 2020年第10258期396卷 1204-1222页

作者： Abbafati, Cristiana Abbas, Kaja M. Abbasi, Mohammad Abbasifard, Mitra Abbasi-Kangevari, Mohsen Abbastabar, Hedayat Abd-Allah, Foad Abdelalim, Ahmed Abdollahi, Mohammad Abdollahpour, Ibrahim Abedi, Aidin Abedi, Parisa Abegaz, Kedir Hussein Abolhassani, Hassan Abosetugn, Akine Eshete Aboyans, Victor Abrams, Elissa M. Abreu, Lucas Guimarães Abrigo, Michael R.M. Abu Haimed, Abdulaziz Khalid Abualhasan, Ahmed Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Abushouk, Abdelrahman I. Acebedo, Alyssa Ackerman, Ilana N. Adabi, Maryam Adair, Tim Adamu, Abdu A. Adebayo, Oladimeji M. Adedeji, Isaac Akinkunmi Adekanmbi, Victor Adelson, Jaimie D. Adeoye, Abiodun Moshood Adetokunboh, Olatunji O. Adham, Davoud Advani, Shailesh M. Afarideh, Mohsen Afshari, Mahdi Afshin, Ashkan Agardh, Emilie E. Agarwal, Gina Agasthi, Pradyumna Agesa, Kareha M. Aghaali, Mohammad Aghamir, Seyed Mohammad Kazem Agrawal, Anurag Ahmad, Tauseef Ahmadi, Alireza Ahmadi, Keivan Ahmadi, Mehdi Ahmadieh, Hamid Ahmadpour, Ehsan Ahmed, Muktar Beshir Aji, Budi Akalu, Temesgen Yihunie Akinyemi, Rufus Olusola Akinyemiju, Tomi Akombi, Blessing Akunna, Chisom Joyqueenet Alahdab, Fares Al-Aly, Ziyad Alam, Khurshid Alam, Noore Alam, Samiah Alam, Tahiya Alanezi, Fahad Mashhour Alanzi, Turki M. Albertson, Samuel B. Alcalde-Rabanal, Jacqueline Elizabeth Alema, Niguse Meles Alemu, Biresaw Wassihun Alemu, Yihun Mulugeta Alhabib, Khalid F. Alhassan, Robert Kaba Ali, Muhammad Ali, Saqib Alicandro, Gianfranco Alijanzadeh, Mehran Alinia, Cyrus Alipour, Vahid Alizade, Hesam Aljunid, Syed Mohamed Alla, François Allebeck, Peter Almadi, Majid Abdulrahman Hamad Almasi, Ali Almasi-Hashiani, Amir Almasri, Nihad A. Al-Mekhlafi, Hesham M. Almulhim, Abdulaziz M. Alonso, Jordi Al-Raddadi, Rajaa M. Altirkawi, Khalid A. Alumran, Arwa Khalid Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Amare, Bekalu Amini, Saeed Amini-Rarani, Mostafa Aminorroaya, Arya Amiri, Fatemeh Amit, Arianna Maever L. Amugsi, Dickson A. Amul, Gianna Gayle Herrera Anbesu, Etsay Woldu Ancuceanu, Robert Anderl Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London United Kingdom Social Determinants of Health Research Center Tehran Iran Department of Neurology Cairo University Cairo Egypt School of Pharmacy Tehran Iran The Institute of Pharmaceutical Sciences (TIPS) Tehran Iran Neuroscience Research Center Isfahan University of Medical Sciences Isfahan Iran Biostatistics Department Near East University Nicosia Cyprus Biostatistics and Health Informatics Madda Walabu University Bale Robe Ethiopia Research Center for Immunodeficiencies Tehran Iran Karolinska University Hospital Huddinge Sweden Department of Cardiology Dupuytren University Hospital Limoges France University of Limoges Limoges France Department of Pediatric Dentistry Federal University of Minas Gerais Belo Horizonte Brazil Department of Research Philippine Institute for Development Studies Quezon City Philippines Department of Healthcare Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Harvard Medical School Boston MA United States Department of Medicine Ain Shams University Cairo Egypt Hamadan University of Medical Sciences Hamadan Iran School of Medicine Cardiff University Cardiff United Kingdom School of Laboratory Medicine and Science University College Hospital Ibadan Ibadan Nigeria Institute of Cardiovascular Diseases University of Ibadan Ibadan Nigeria Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Cape Town South Africa Department of Global Health Stellenbosch University Cape Town South Africa School of Health Ardabil University of Medical Science Ardabil Iran Social Behavioral Research Branch National Institute of Health Bethesda MD United States Department of Oncology Georgetown University Washington DC DC United States Department of Health Metrics Sciences School of Medicine Seattle WA

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw value

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