Feedback Control for Personalized Medicine

Feedback Control for Personalized Medicine

1st Edition - April 21, 2022

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  • Editor: Esteban Hernandez-Vargas
  • Paperback ISBN: 9780323901710
  • eBook ISBN: 9780323906654

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Description

Feedback Control for Personalized Medicine provides ideas on ongoing efforts and obstacles by members of the control engineering community in different biological and medical applications. In addition, the book presents key challenges, insights, tools and theoretical developments that arise from personalized medicine, along with medical concepts that are explained by engineers to help non-experts follow research topics. Several clinical trials have tried to find therapeutic approaches to achieve eradication or at least lifelong, therapy-free, host control of the infection. This has been performed integrating clinical observations, empirical knowledge and information from medical tests to treat patients. As this “trial and error” approach is becoming more challenging and unfeasible by the steep increase in the number of different pieces of information and the complexity of large datasets,  a systematic and tractable approach that integrates a variety of biological and medical research data into mathematical models and computational algorithms is crucial to harness knowledge and to develop new therapies towards personalized medicine.

Key Features

  • Presents the most recent research in personalized medicine using control theoretical tools
  • Offers numerical simulations that are analyzed in detail and compared with control experiments
  • Brings the most recent research of control theory in medicine

Readership

Researchers in Industry and Academia in electronical engineers, biomedical engineers as well as computer scientists to follow the trends in control theoretical approaches in biomedical engineering. Graduate students that have as a research topic control theory and/or biomedical engineering

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Dedication
  • Contributors
  • About the editor
  • Preface
  • Acknowledgments
  • Chapter 1: Closing the loop in personalized medicine
  • Abstract
  • References
  • Chapter 2: Optimal control strategies to tailor antivirals for acute infectious diseases in the host: a study case of COVID-19
  • Abstract
  • 2.1. Introduction
  • 2.2. Review of the target cell limited model for in-host infection
  • 2.3. Equilibrium characterization and stability
  • 2.4. Inclusion of PK and PD of antiviral treatment
  • 2.5. Control strategies to tailor therapies
  • 2.6. Simulation results
  • 2.7. Conclusions and future works
  • References
  • Chapter 3: Input-output approaches for personalized drug dosing of antibiotics
  • Abstract
  • Acknowledgements
  • 3.1. Introduction
  • 3.2. Population pharmacokinetic model
  • 3.3. Individualized drug dosing
  • 3.4. State estimation and feedback control law
  • 3.5. Conclusion
  • References
  • Chapter 4: Safe glycemia regulation considering parameter variations under the offset-free MPC with pulse inputs scheme
  • Abstract
  • 4.1. Introduction
  • 4.2. System with pulse inputs
  • 4.3. Offset-free MPC strategy
  • 4.4. Application to T1DM treatment
  • 4.5. Safety layer for insulin-on-board constraint
  • 4.6. Conclusion
  • References
  • Chapter 5: Deep neuronal network-based glucose prediction for personalized medicine
  • Abstract
  • Acknowledgements
  • 5.1. Introduction
  • 5.2. Deep neural networks
  • 5.3. Direct multistep ahead prediction strategy
  • 5.4. System description
  • 5.5. Results
  • 5.6. Conclusion and discussion
  • References
  • Chapter 6: Control-based drug tailoring schemes towards personalized influenza treatment
  • Abstract
  • 6.1. Introduction
  • 6.2. Influenza virus in the host
  • 6.3. Influenza treatment and therapy optimization
  • 6.4. Control schemes towards treatment tailoring
  • 6.5. Inverse optimal impulsive control
  • 6.6. Influenza treatment tailoring
  • 6.7. Conclusions and final remarks
  • References
  • Chapter 7: Polynomial state estimation in infectious diseases
  • Abstract
  • 7.1. Introduction
  • 7.2. Preliminaries
  • 7.3. Statement of the problem
  • 7.4. Solution of the problem
  • 7.5. State estimation for systems of viral infections
  • 7.6. Conclusions
  • References
  • Chapter 8: Sliding mode control theory interprets elite control of HIV
  • Abstract
  • Acknowledgements
  • 8.1. Introduction
  • 8.2. Modeling HIV infection in vivo
  • 8.3. A reachability condition for elite control of HIV infection
  • 8.4. Robustness properties of elite control of HIV infection
  • 8.5. Simulation results
  • 8.6. Conclusion
  • References
  • Chapter 9: A stochastic model for hepatitis C viral infection dynamics with the innate immune response
  • Abstract
  • 9.1. Introduction
  • 9.2. Biological background
  • 9.3. Stochastic model
  • 9.4. Results
  • 9.5. Conclusion
  • Conflict of interest statement
  • Author contributions
  • Funding
  • References
  • Chapter 10: Impulsive nonlinear MPC with application to oncolytic virus therapy
  • Abstract
  • 10.1. Introduction
  • 10.2. Notation
  • 10.3. Model of oncolytic virus therapy
  • 10.4. Model predictive control for oncolytic virus therapy
  • 10.5. Results
  • 10.6. Conclusion
  • References
  • Chapter 11: Is the isolated heart a relaxation-oscillator?
  • Abstract
  • 11.1. Introduction
  • 11.2. Biological background
  • 11.3. Theoretical background
  • 11.4. Methodology
  • 11.5. Results
  • 11.6. Conclusion
  • Conflict of interest statement
  • Author contributions
  • Funding
  • References
  • Index

Product details

  • No. of pages: 246
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: April 21, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323901710
  • eBook ISBN: 9780323906654

About the Editor

Esteban Hernandez-Vargas

Esteban Abelardo Hernandez Vargas. Associate Professor, Institute of Mathematics, UNAM, Mexico | Research Fellow, Frankfurt Institute for Advanced Studies, Germany. Esteban moved to Ireland to pursue his PhD in Mathematics at the Hamilton Institute, NUI, Ireland. After completing his doctoral studies in 2011, he continued his research as a postdoctoral fellow (2011-2014) at the Helmholtz Centre for Infection Research, Braunschweig, Germany. In summer 2014, he founded the research group of Systems Medicine of Infectious Diseases at the Helmholtz Centre for Infection Research. In March 2017, his research group was relocated to the Frankfurt Institute for Advanced Studies in Frankfurt am Main, Germany. Since January 2020, he is also Professor at the National Autonomous University of Mexico (UNAM). Additionally, he was adjunct lecturer at the Otto-von-Guericke-Universität Magdeburg as well as a visiting scholar at Los Alamos National Laboratory, Universidad de Guadalajara and CentraleSupelec. He has published more than 100 peer-reviewed research articles, and one book in infectious diseases.

Affiliations and Expertise

Associate Professor, Institute of Mathematics, UNAM, Mexico; Research Fellow, Frankfurt Institute for Advanced Studies, Germany

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