Ghent University Academic Bibliography

Advanced

A computationally efficient Hill curve adaptation strategy during continuous monitoring of dose-effect relation in anaesthesia

Clara-Mihaela Ionescu UGent (2018) NONLINEAR DYNAMICS. 92(3). p.843-852
abstract
This paper discusses a possibility to simplify the number of parameters in the Hill curve by exploiting special mathematical functions. This simplification is relevant when adaptation is required for personalized model-based medicine during continuous monitoring of dose-response values. A mathematical framework of the involved physiology and modelling by means of distributed parameter progressions has been employed. Convergence to a unique dynamic response is achieved, allowing simplifying assumptions with guaranteed solution. Discussion on its use and comparison with other adaptation mechanism is provided.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
keyword
Hill curve, Continuous fraction expansion, Mathematical model, Nonlinear dynamics, Variability, Dose-effect relation, Patient specificity
journal title
NONLINEAR DYNAMICS
volume
92
issue
3
pages
843 - 852
Web of Science type
Article
Web of Science id
000429555200005
ISSN
0924-090X
1573-269X
DOI
10.1007/s11071-018-4095-3
language
English
UGent publication?
yes
classification
A1
id
8563698
handle
http://hdl.handle.net/1854/LU-8563698
date created
2018-05-31 12:43:29
date last changed
2018-07-10 06:40:07
@article{8563698,
  abstract     = {This paper discusses a possibility to simplify the number of parameters in the Hill curve by exploiting special mathematical functions. This simplification is relevant when adaptation is required for personalized model-based medicine during continuous monitoring of dose-response values. A mathematical framework of the involved physiology and modelling by means of distributed parameter progressions has been employed. Convergence to a unique dynamic response is achieved, allowing simplifying assumptions with guaranteed solution. Discussion on its use and comparison with other adaptation mechanism is provided.},
  author       = {Ionescu, Clara-Mihaela},
  issn         = {0924-090X},
  journal      = {NONLINEAR DYNAMICS},
  keyword      = {Hill curve,Continuous fraction expansion,Mathematical model,Nonlinear dynamics,Variability,Dose-effect relation,Patient specificity},
  language     = {eng},
  number       = {3},
  pages        = {843--852},
  title        = {A computationally efficient Hill curve adaptation strategy during continuous monitoring of dose-effect relation in anaesthesia},
  url          = {http://dx.doi.org/10.1007/s11071-018-4095-3},
  volume       = {92},
  year         = {2018},
}

Chicago
Ionescu, Clara-Mihaela. 2018. “A Computationally Efficient Hill Curve Adaptation Strategy During Continuous Monitoring of Dose-effect Relation in Anaesthesia.” Nonlinear Dynamics 92 (3): 843–852.
APA
Ionescu, C.-M. (2018). A computationally efficient Hill curve adaptation strategy during continuous monitoring of dose-effect relation in anaesthesia. NONLINEAR DYNAMICS, 92(3), 843–852.
Vancouver
1.
Ionescu C-M. A computationally efficient Hill curve adaptation strategy during continuous monitoring of dose-effect relation in anaesthesia. NONLINEAR DYNAMICS. 2018;92(3):843–52.
MLA
Ionescu, Clara-Mihaela. “A Computationally Efficient Hill Curve Adaptation Strategy During Continuous Monitoring of Dose-effect Relation in Anaesthesia.” NONLINEAR DYNAMICS 92.3 (2018): 843–852. Print.