Konstantinos Georgatzis,
Chris Williams,
Christopher Hawthorne
;
Proceedings of the 1st Machine Learning for Healthcare Conference, PMLR 56:1-16, 2016.
Abstract
We present a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients admitted in Intensive Care Units (ICUs). More specifically we are interested in modelling the effect of a widely used anaesthetic drug (Propofol) on a patient’s monitored depth of anaesthesia and haemodynamics. We compare our approach with one from the Pharmacokinetics/Pharmacodynamics (PK/PD) literature and show that we can provide significant improvements in performance without requiring the incorporation of expert physiological knowledge in our system.
@InProceedings{pmlr-v56-Georgatzis16,
title = {Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring},
author = {Konstantinos Georgatzis and Chris Williams and Christopher Hawthorne},
booktitle = {Proceedings of the 1st Machine Learning for Healthcare Conference},
pages = {1--16},
year = {2016},
editor = {Finale Doshi-Velez and Jim Fackler and David Kale and Byron Wallace and Jenna Wiens},
volume = {56},
series = {Proceedings of Machine Learning Research},
address = {Children's Hospital LA, Los Angeles, CA, USA},
month = {18--19 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v56/Georgatzis16.pdf},
url = {http://proceedings.mlr.press/v56/Georgatzis16.html},
abstract = {We present a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients admitted in Intensive Care Units (ICUs). More specifically we are interested in modelling the effect of a widely used anaesthetic drug (Propofol) on a patient’s monitored depth of anaesthesia and haemodynamics. We compare our approach with one from the Pharmacokinetics/Pharmacodynamics (PK/PD) literature and show that we can provide significant improvements in performance without requiring the incorporation of expert physiological knowledge in our system.}
}
%0 Conference Paper
%T Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring
%A Konstantinos Georgatzis
%A Chris Williams
%A Christopher Hawthorne
%B Proceedings of the 1st Machine Learning for Healthcare Conference
%C Proceedings of Machine Learning Research
%D 2016
%E Finale Doshi-Velez
%E Jim Fackler
%E David Kale
%E Byron Wallace
%E Jenna Wiens
%F pmlr-v56-Georgatzis16
%I PMLR
%J Proceedings of Machine Learning Research
%P 1--16
%U http://proceedings.mlr.press
%V 56
%W PMLR
%X We present a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients admitted in Intensive Care Units (ICUs). More specifically we are interested in modelling the effect of a widely used anaesthetic drug (Propofol) on a patient’s monitored depth of anaesthesia and haemodynamics. We compare our approach with one from the Pharmacokinetics/Pharmacodynamics (PK/PD) literature and show that we can provide significant improvements in performance without requiring the incorporation of expert physiological knowledge in our system.
TY - CPAPER
TI - Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring
AU - Konstantinos Georgatzis
AU - Chris Williams
AU - Christopher Hawthorne
BT - Proceedings of the 1st Machine Learning for Healthcare Conference
PY - 2016/12/10
DA - 2016/12/10
ED - Finale Doshi-Velez
ED - Jim Fackler
ED - David Kale
ED - Byron Wallace
ED - Jenna Wiens
ID - pmlr-v56-Georgatzis16
PB - PMLR
SP - 1
DP - PMLR
EP - 16
L1 - http://proceedings.mlr.press/v56/Georgatzis16.pdf
UR - http://proceedings.mlr.press/v56/Georgatzis16.html
AB - We present a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients admitted in Intensive Care Units (ICUs). More specifically we are interested in modelling the effect of a widely used anaesthetic drug (Propofol) on a patient’s monitored depth of anaesthesia and haemodynamics. We compare our approach with one from the Pharmacokinetics/Pharmacodynamics (PK/PD) literature and show that we can provide significant improvements in performance without requiring the incorporation of expert physiological knowledge in our system.
ER -
Georgatzis, K., Williams, C. & Hawthorne, C.. (2016). Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring. Proceedings of the 1st Machine Learning for Healthcare Conference, in PMLR 56:1-16
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