Feedback control for personalized medicine

Feedback control for personalized medicine /

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Bibliographic Details
Corporate Author: ProQuest (Firm)
Other Authors: Hernandez-Vargas, Esteban A.
Format: eBook
Language:English
Published: London, UK : Academic Press, 2022.
Subjects:
Biomedical engineering.
Precision medicine.
Feedback control systems.
Precision Medicine
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)
Table of Contents:
  • Front Cover
  • Feedback Control for Personalized Medicine
  • Copyright
  • Contents
  • Contributors
  • About the editor
  • Preface
  • Acknowledgments
  • 1 Closing the loop in personalized medicine
  • References
  • 2 Optimal control strategies to tailor antivirals for acute infectious diseases in the host: a study case of COVID-19
  • 2.1 Introduction
  • 2.1.1 Notation
  • 2.2 Review of the target cell limited model for in-host infection
  • 2.3 Equilibrium characterization and stability
  • 2.3.1 Asymptotic stability of the equilibrium sets
  • 2.3.2 Stability theory
  • 2.6.3 Quasioptimal single interval treatment
  • 2.6.4 Short-term treatment
  • 2.6.5 Two-step treatment, lowering the peak of V
  • 2.7 Conclusions and future works
  • References
  • 3 Input-output approaches for personalized drug dosing of antibiotics
  • 3.1 Introduction
  • 3.2 Population pharmacokinetic model
  • 3.2.1 State-space representation
  • 3.2.2 System analysis
  • 3.2.3 Case study: model of meropenem
  • 3.3 Individualized drug dosing
  • 3.3.1 Input-output analysis
  • 3.3.2 Input-output formula for drug dosing
  • 3.3.3 ``Worst-case'' analysis
  • 3.3.4 PTA analysis
  • 4.5.3 The switching signal
  • 4.5.4 Coupling the safety layer with the offset-free strategy
  • 4.6 Conclusion
  • References
  • 5 Deep neuronal network-based glucose prediction for personalized medicine
  • 5.1 Introduction
  • 5.2 Deep neural networks
  • 5.2.1 Recurrent neuronal networks
  • 5.2.2 Long short-term memory recurrent neural network
  • 5.2.3 Bidirectional LSTM
  • 5.2.4 Multilayer networks
  • 5.3 Direct multistep ahead prediction strategy
  • 5.4 System description
  • 5.4.1 Dataset description
  • 5.4.2 Neuronal network configuration using CGM
  • 5.5 Results
  • 5.6 Conclusion and discussion

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