A multiparametric model‐based optimization and control approach to anaesthesia

Nașcu, Ioan; Pistikopoulos, Efstratios N.

doi:10.1002/cjce.22634

Cited by 4 publications

(3 citation statements)

References 77 publications

(105 reference statements)

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“…The other issue comes from Equations ( 5) and (7). These two equations are delay differential equations.…”

Section: Time Delay In Pd Equationsmentioning

confidence: 99%

“…[6] Na cu and Pistikopoulos applied model predictive controllers to the intravenous anaesthesia process. [7] To help physicians make patient-specific decisions on the optimal dosage of EPO treatment, a model that describes the Hgb response to the EPO dosing is necessary. Existing methods of erythropoiesis modelling can be divided into two main categories.…”

Section: Introductionmentioning

confidence: 99%

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Haemoglobin response modelling under erythropoietin treatment: Physiological model‐informed machine learning method

Zhang

2023

Can J Chem Eng

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Patients with renal anaemia are usually treated with recombinant human erythropoietin (EPO) because of insufficient renal EPO secretion. The establishment of a good haemoglobin (Hgb) response model is a necessary condition for dose optimization design. The purpose of this paper is to apply physics‐informed neural networks (PINN) to build the Hgb response model under EPO treatment. Neural network training is guided by a physiological model to avoid overfitting problems. During the training process, the parameters of the physiological model can be estimated simultaneously. To handle differential equations with impulse inputs and time delays, we propose approximate model equations for the pharmacokinetic (PK) model and the pharmacodynamic (PD) model, respectively. The modified PK/PD model was incorporated into PINN for training. Tests on simulated data and clinical data show that the proposed method has better performance than data‐driven modelling methods and the traditional physiological modelling based on the least squares method.

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“…The other issue comes from Equations ( 5) and (7). These two equations are delay differential equations.…”

Section: Time Delay In Pd Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Haemoglobin response modelling under erythropoietin treatment: Physiological model‐informed machine learning method

Zhang

2023

Can J Chem Eng

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“…into the input command and the feedback signal is a common approach to cancel the nonlinear part of the anesthesia system [284]. Other techniques such as linear model predictive control (LMPC) [285].…”

Section: Automation In Anesthesiamentioning

confidence: 99%

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

et al. 2020

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Over the past decade, there has been an unprecedented international focus on improved quality and availability of medical care, which has reignited interest in clinical automation and drawn researchers toward novel solutions in the field of physiological closed-loop control systems (PCLCs). Today, multidisciplinary groups of expert scientists, engineers, clinicians, mathematicians, and policy-makers are combining their knowledge and experience to develop both the next generation of PCLC-based medical equipment and a collaborative commercial/academic infrastructure to support this rapidly expanding frontier. In the following article, we provide a robust introduction to the various aspects of this growing field motivated by the recent and ongoing work supporting two leading technologies: the artificial pancreas (AP) and automated anesthesia. Following a brief high-level overview of the main concepts in automated therapy and some relevant tools from systems and control theory, we explore -separately -the developments, challenges, state-ofthe-art, and probable directions for AP and automated anesthesia systems. We then close the review with a consideration of the common lessons gleaned from these ventures and the implications they present for future investigations and adjacent research.

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