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.
1. Feedback Control for Personalized Medicine - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Dedication - p. Page iv 7. Contents - p. Page vi 8. Contributors - p. Page vi 9. About the editor - p. Page vii 10. Preface - p. Page viii 11. Acknowledgments - p. Page ix 12. List of Illustrations - p. Page xi 13. List of Tables - p. Page xii 14. Chapter 1 : Closing the loop in personalized medicine - p. Page xii 15. Chapter 2 : Optimal control strategies to tailor antivirals for acute infectious diseases in the host: a study case of COVID-19 - p. Page 7 16. Chapter 3 : Input-output approaches for personalized drug dosing of antibiotics - p. Page 37 17. Chapter 4 : Safe glycemia regulation considering parameter variations under the offset-free MPC with pulse inputs scheme - p. Page 64 18. Chapter 5 : Deep neuronal network-based glucose prediction for personalized medicine - p. Page 84 19. Chapter 6 : Control-based drug tailoring schemes towards personalized influenza treatment - p. Page 104 20. Chapter 7 : Polynomial state estimation in infectious diseases - p. Page 124 21. Chapter 8 : Sliding mode control theory interprets elite control of HIV - p. Page 147 22. Chapter 9 : A stochastic model for hepatitis C viral infection dynamics with the innate immune response - p. Page 169 23. Chapter 10 : Impulsive nonlinear MPC with application to oncolytic virus therapy - p. Page 185 24. Chapter 11 : Is the isolated heart a relaxation-oscillator? - p. Page 200 25. Index - p. Page 216
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.
1. Feedback Control for Personalized Medicine - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Dedication - p. Page iv 7. Contents - p. Page vi 8. Contributors - p. Page vi 9. About the editor - p. Page vii 10. Preface - p. Page viii 11. Acknowledgments - p. Page ix 12. List of Illustrations - p. Page xi 13. List of Tables - p. Page xii 14. Chapter 1 : Closing the loop in personalized medicine - p. Page xii 15. Chapter 2 : Optimal control strategies to tailor antivirals for acute infectious diseases in the host: a study case of COVID-19 - p. Page 7 16. Chapter 3 : Input-output approaches for personalized drug dosing of antibiotics - p. Page 37 17. Chapter 4 : Safe glycemia regulation considering parameter variations under the offset-free MPC with pulse inputs scheme - p. Page 64 18. Chapter 5 : Deep neuronal network-based glucose prediction for personalized medicine - p. Page 84 19. Chapter 6 : Control-based drug tailoring schemes towards personalized influenza treatment - p. Page 104 20. Chapter 7 : Polynomial state estimation in infectious diseases - p. Page 124 21. Chapter 8 : Sliding mode control theory interprets elite control of HIV - p. Page 147 22. Chapter 9 : A stochastic model for hepatitis C viral infection dynamics with the innate immune response - p. Page 169 23. Chapter 10 : Impulsive nonlinear MPC with application to oncolytic virus therapy - p. Page 185 24. Chapter 11 : Is the isolated heart a relaxation-oscillator? - p. Page 200 25. Index - p. Page 216
