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dc.contributor.advisorWilliams, Chrisen
dc.contributor.advisorRogers, Simonen
dc.contributor.authorGeorgatzis, Konstantinosen
dc.date.accessioned2018-03-16T12:01:57Z
dc.date.available2018-03-16T12:01:57Z
dc.date.issued2017-11-30
dc.identifier.urihttp://hdl.handle.net/1842/28838
dc.description.abstractIntensive Care Units (ICUs) host patients in critical condition who are being monitored by sensors which measure their vital signs. These vital signs carry information about a patient’s physiology and can have a very rich structure at fine resolution levels. The task of analysing these biosignals for the purposes of monitoring a patient’s physiology is referred to as physiological condition monitoring. Physiological condition monitoring of patients in ICUs is of critical importance as their health is subject to a number of events of interest. For the purposes of this thesis, the overall task of physiological condition monitoring is decomposed into the sub-tasks of modelling a patient’s physiology a) under the effect of physiological or artifactual events and b) under the effect of drug administration. The first sub-task is concerned with modelling artifact (such as the taking of blood samples, suction events etc.), and physiological episodes (such as bradycardia), while the second sub-task is focussed on modelling the effect of drug administration on a patient’s physiology. The first contribution of this thesis is the formulation, development and validation of the Discriminative Switching Linear Dynamical System (DSLDS) for the first sub-task. The DSLDS is a discriminative model which identifies the state-of-health of a patient given their observed vital signs using a discriminative probabilistic classifier, and then infers their underlying physiological values conditioned on this status. It is demonstrated on two real-world datasets that the DSLDS is able to outperform an alternative, generative approach in most cases of interest, and that an a-mixture of the two models achieves higher performance than either of the two models separately. The second contribution of this thesis is the formulation, development and validation of the Input-Output Non-Linear Dynamical System (IO-NLDS) for the second sub-task. The IO-NLDS is a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients. More specifically, in this thesis the focus is on modelling the effect of the widely used anaesthetic drug Propofol on a patient’s monitored depth of anaesthesia and haemodynamics. A comparison of the IO-NLDS with a model derived from the Pharmacokinetics/Pharmacodynamics (PK/PD) literature on a real-world dataset shows that significant improvements in predictive performance can be provided without requiring the incorporation of expert physiological knowledge.en
dc.contributor.sponsorEngineering and Physical Sciences Research Council (EPSRC)en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.relation.hasversionGeorgatzis, K. andWilliams, C. K. I. (2015). Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring. In Proceedings of the Thirtyfirst Conference on Uncertainty in Artificial Intelligence (UAI), pages 306–315.en
dc.relation.hasversionGeorgatzis, K., Lal, P., Hawthorne, C., Shaw, M., Piper, I., Tarbert, C., Donald, R., and Williams, C. K. I. (2016a). Artefact in Physiological Data Collected from Patients with Brain Injury: Quantifying the Problem and Providing a Solution Using a Factorial Switching Linear Dynamical Systems Approach. Intracranial Pressure and Brain Monitoring XV, Acta Neurochirurgica Journal Supplement, 122(1), 301–305.en
dc.relation.hasversionGeorgatzis, K., Williams, C. K. I., and Hawthorne, C. (2016b). Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring. Proceedings of Machine Learning in Healthcare, JMLR W&C Track, Volume 56.en
dc.relation.hasversionLal, P., Williams, C. K. I., Georgatzis, K., Hawthorne, C., McMonagle, P., Piper, I., and Shaw, M. (2015). Detecting Artifactual Events in Vital Signs Monitoring Data. Technical report, University of Edinburgh.en
dc.subjectDiscriminative Switching Linear Dynamical Systemen
dc.subjectDSLDSen
dc.subjectdiscriminative probabilistic classifieren
dc.subjectInput-Output Non-Linear Dynamical Systemen
dc.subjectIO-NLDSen
dc.subjectnon-linear dynamical systemen
dc.subjectmodellingen
dc.titleDynamical probabilistic graphical models applied to physiological condition monitoringen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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