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
