Dynamical probabilistic graphical models applied to physiological condition monitoring
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Date
30/11/2017Author
Georgatzis, Konstantinos
Metadata
Abstract
Intensive 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.