Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v56-Georgatzis16, title = {Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring}, author = {Georgatzis, Konstantinos and Williams, Chris and Hawthorne, Christopher}, booktitle = {Proceedings of the 1st Machine Learning for Healthcare Conference}, pages = {1--16}, year = {2016}, editor = {Doshi-Velez, Finale and Fackler, Jim and Kale, David and Wallace, Byron and Wiens, Jenna}, volume = {56}, series = {Proceedings of Machine Learning Research}, address = {Northeastern University, Boston, MA, USA}, month = {18--19 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v56/Georgatzis16.pdf}, url = {https://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.} }
Endnote
%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 %P 1--16 %U https://proceedings.mlr.press/v56/Georgatzis16.html %V 56 %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.
RIS
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 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 DP - Proceedings of Machine Learning Research VL - 56 SP - 1 EP - 16 L1 - http://proceedings.mlr.press/v56/Georgatzis16.pdf UR - https://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 -
APA
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 Proceedings of Machine Learning Research 56:1-16 Available from https://proceedings.mlr.press/v56/Georgatzis16.html.

Related Material