Uncertainty minimization for systems with measurable disturbance : study case of anesthesia-hemodynamic interactions
- Author
- Clara Ionescu (UGent) and Robain De Keyser (UGent)
- Organization
- Project
-
- Personalized therapy for lung cancer patients using respiratory impedance models
- Modelling and control of Non-Newtonian fluids (MOCONEF)
- A complete drug regulatory guide for surgical interventions under general anesthesia
- Modelling and validation of analgesia prediction for computer-guided TIVA systems
- Development of a generic patient parameterizable model to describe pain-relief levels during general anesthesia.
- Validation of mathematical model of pain characterisation in awake post-operative patients undergoing pain treatment
- Minimal information model multi-objective predictive control for multivariable systems (MIMOPREC)
- Conacon: Context Aware Control
- Abstract
- In this paper, a novel control methodology is presented to regulate the complex anesthetic-hemodynamic interaction during general anesthesia by means of predictive control algorithm. The proposed framework minimizes the risk of instability arising from large uncertainty in the patient model dynamics and external disturbances such as surgical events. Identification is very difficult and only a limited amount of data can be actually used during the startup of the anesthesia as to calibrate the generic patient models. There is a crucial need to explore additional ways to minimize uncertainty in the closed loop. The paper introduces a natural mimicking strategy of actual anesthesiologists real-life decision making procedures. The framework is tested in an open access patient simulator for hypnotic drug delivery management in general anesthesia.
- Keywords
- GENERAL-ANESTHESIA, LOOP, ROBUSTNESS, PROPOFOL, DESIGN
Downloads
-
root.pdf
- full text (Accepted manuscript)
- |
- open access
- |
- |
- 373.11 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01JEE19Y6YW6QN4GWST520CPPE
- MLA
- Ionescu, Clara, and Robain De Keyser. “Uncertainty Minimization for Systems with Measurable Disturbance : Study Case of Anesthesia-Hemodynamic Interactions.” 2022 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS, CCTA, IEEE, 2022, pp. 1093–98, doi:10.1109/ccta49430.2022.9966190.
- APA
- Ionescu, C., & De Keyser, R. (2022). Uncertainty minimization for systems with measurable disturbance : study case of anesthesia-hemodynamic interactions. 2022 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS, CCTA, 1093–1098. https://doi.org/10.1109/ccta49430.2022.9966190
- Chicago author-date
- Ionescu, Clara, and Robain De Keyser. 2022. “Uncertainty Minimization for Systems with Measurable Disturbance : Study Case of Anesthesia-Hemodynamic Interactions.” In 2022 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS, CCTA, 1093–98. IEEE. https://doi.org/10.1109/ccta49430.2022.9966190.
- Chicago author-date (all authors)
- Ionescu, Clara, and Robain De Keyser. 2022. “Uncertainty Minimization for Systems with Measurable Disturbance : Study Case of Anesthesia-Hemodynamic Interactions.” In 2022 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS, CCTA, 1093–1098. IEEE. doi:10.1109/ccta49430.2022.9966190.
- Vancouver
- 1.Ionescu C, De Keyser R. Uncertainty minimization for systems with measurable disturbance : study case of anesthesia-hemodynamic interactions. In: 2022 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS, CCTA. IEEE; 2022. p. 1093–8.
- IEEE
- [1]C. Ionescu and R. De Keyser, “Uncertainty minimization for systems with measurable disturbance : study case of anesthesia-hemodynamic interactions,” in 2022 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS, CCTA, Trieste, Italy, 2022, pp. 1093–1098.
@inproceedings{01JEE19Y6YW6QN4GWST520CPPE,
abstract = {{In this paper, a novel control methodology is presented to regulate the complex anesthetic-hemodynamic interaction during general anesthesia by means of predictive control algorithm. The proposed framework minimizes the risk of instability arising from large uncertainty in the patient model dynamics and external disturbances such as surgical events. Identification is very difficult and only a limited amount of data can be actually used during the startup of the anesthesia as to calibrate the generic patient models. There is a crucial need to explore additional ways to minimize uncertainty in the closed loop. The paper introduces a natural mimicking strategy of actual anesthesiologists real-life decision making procedures. The framework is tested in an open access patient simulator for hypnotic drug delivery management in general anesthesia.}},
author = {{Ionescu, Clara and De Keyser, Robain}},
booktitle = {{2022 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS, CCTA}},
isbn = {{9781665473385}},
issn = {{2768-0762}},
keywords = {{GENERAL-ANESTHESIA,LOOP,ROBUSTNESS,PROPOFOL,DESIGN}},
language = {{eng}},
location = {{Trieste, Italy}},
pages = {{1093--1098}},
publisher = {{IEEE}},
title = {{Uncertainty minimization for systems with measurable disturbance : study case of anesthesia-hemodynamic interactions}},
url = {{http://doi.org/10.1109/ccta49430.2022.9966190}},
year = {{2022}},
}
- Altmetric
- View in Altmetric
- Web of Science
- Times cited: