scholarly journals Identifiability of pharmacological models for online individualization

2021 ◽  
Author(s):  
Ylva Wahlquist ◽  
Amina Gojak ◽  
Kristian Soltesz
Keyword(s):  
Closed Loop ◽  
Induction Phase ◽  
Theoretic Analysis ◽  
Improve Performance ◽  
Large Variability ◽  
Drug Dosing ◽  
Response Data ◽  
Anesthetic Drugs ◽  
Phase Data ◽  
Pharmacological Model

There is a large variability between individuals in the response to anesthetic drugs, that seriously limits the achievable performance of closed-loop controlled drug dosing. Full individualization of patient models based on early clinical response data has been suggested as a means to improve performance with maintained robustness (safety). We use estimation theoretic analysis and realization theory to characterize practical identifiability of the standard pharmacological model structure from anesthetic induction phase data and conclude that such approaches are not practically feasible.

Data-driven I-PD Gain Tuning using Closed-loop Step Response Data

2019 ◽  
Vol 139 (8) ◽  
pp. 882-888
Author(s):  
Shiro Masuda ◽  
Jongho Park ◽  
Yoshihiro Matsui
Keyword(s):  
Closed Loop ◽  
Data Driven ◽  
Step Response ◽  
Response Data

Mode-Based Controller Design for Hammerstein Models Using Closed-Loop Tranjent Response Data

2016 ◽  
Vol 136 (5) ◽  
pp. 625-632
Author(s):  
Yoshihiro Matsui ◽  
Hideki Ayano ◽  
Shiro Masuda ◽  
Kazushi Nakano
Keyword(s):  
Closed Loop ◽  
Controller Design ◽  
Response Data ◽  
Hammerstein Models

Control-Theoretic Analysis of a Closed-Loop PPC System

CIRP Annals ◽  
2002 ◽  
Vol 51 (1) ◽  
pp. 379-382 ◽  
Author(s):  
N. Duffie ◽  
I. Falu
Keyword(s):  
Closed Loop ◽  
Theoretic Analysis

Estimation of response curves in closed-loop physiological control

1986 ◽  
Vol 61 (4) ◽  
pp. 1481-1491 ◽  
Author(s):  
C. S. Poon
Keyword(s):  
Least Squares ◽  
Closed Loop ◽  
Ordinary Least Squares ◽  
Repeated Measurements ◽  
System Structure ◽  
Paired Data ◽  
Response Curves ◽  
Response Data ◽  
Error Components ◽  
Response Slope

Several recent reports have addressed the problem of estimating the response slope from repeated measurements of paired data when both stimulus and response variables are subject to biological variability. These earlier approaches suffer from several drawbacks: useful information about the relationships between the error components in a closed-loop system is not fully utilized; the response intercept cannot be directly estimated; and the normalization procedure required in some methods may fail under certain circumstances. This paper proposes a new, general method of simultaneously estimating the response slope and intercept from corrupted stimulus-response data when the errors in both variables are specifically related by the system structure. A direct extension of the least-squares approach, this method [directed least squares (DLS)] reduces to ordinary least-squares methods when either of the measured variables is error free and to the reduced-major-axis (RMA) method of Kermack and Haldane (Biometrics 37: 30-41, 1950) when the magnitudes of the normalized errors are equal. The DLS estimators are scale invariant, statistically unbiased and always assume the minimum variance. With simple modifications, the method is also applicable to paired data. If, however, the relation between error components is uncertain, then the RMA method is optimal, i.e., having the least possible asymptotic bias and variance. These results are illustrated by using various types of closed-loop respiratory response data.

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