NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

New Analysis Techniques for ICU Data:
Allocating Uncertainty over Time
Enda O’Connor, Michael G. Madden
College of Engineering and Informatics, National University of Ireland, Galway
e.oconnor2@nuigalway.ie, Michael.Madden@nuigalway.ie
Abstract
This goal of this project is to optimise the performance
of a dynamic Bayesian network by varying how
uncertainty is applied across each node. The approach
taken is inspired by simulated annealing [4] which is an
optimization technique. There are many different types
of uncertainty involved in these model types, however
the focus of this research will be data uncertainty and
the model parameter uncertainty and the trade off
between the two over time.
1. Introduction
A key problem being dealt with in the course of this
research is how an ICU patient can vary greatly over
time from a healthy person but also from other patients.
In order to overcome this issue it is proposed that the
uncertainty applied to dynamic Bayesian network is
alter and varied over time for both model parameters
and data.
The reasoning for this is that data is used to calibrate
a model to a patient, however it is possible that the data
used contains uncertainty. If the models are well
calibrated and the measurements do not fit well it is
possible to apply greater uncertainty to the data and say
the measurements are at fault. During the calibration
period it is a wish to balance both types of uncertainty.
This will be done by applying greater uncertainty to
the model parameters at first as we trust the measured
values as this is the only information available, then in
the later stages greater uncertainty is applied to the data
as we trust the model parameters are a good fit and we
are accounting for any numerical errors due to incorrect
measurements in the data.
To test this we will use the models proposed by
Enright et al [1,2]. These models are a dynamic
Bayesian network representation of Bergman’s ICU-
MM which is explained by Van Herpe et al [3].
2. Methods
The approach being used here involves using the
abstract example proposed by Enright et al [2]. A
number of tests were carried out by altering the
standard deviation and mean in order to see what results
were obtained.
It was found that the model will converge even
when starting values are not correct. When a very
narrow standard deviation was applied to the model it
would not behave correctly and would reset at certain
time-steps as there was no evidence matching the
samples generated by the model. However with larger
standard deviations it was found that the variables could
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retune and from a current approximation and fit with a
later value that matches the evidence. To do this an
optimization technique will be applied to vary the
uncertainty and produce the best possible outcome.
3. Conclusions
This project aims to deal with the optimising the
output of a dynamic Bayesian network. The data being
used is targeted specifically at the problem of blood
glucose levels however, it would be ideal to apply this
research to many other data sets using a dynamic
Bayesian network. There are two core concepts that
need to be studied and understood; probabilistic
reasoning over time and optimisation techniques.
4. References
[1] C. G. Enright, M. G. Madden, S. J. Russell, N. Aleks, G.
Manley, J. Laffey, B. Harte, A. Mulvey, N. Madden.
Modelling Glycaemia in ICU Patients: A Dynamic Bayesian
Network Approach. 2010.
[2] C. G. Enright, M. G. Madden, N. Madden. Derivation of
DBN Structure from Expert Knowledge in the Form of
Mathematical Models. 2011.
[3] A minimal model for glycemia control in critically ill
patients. T. Van Herpe, B. Pluymers, M. Espinoza, G. Van
den Berghe, B. De Moor. New York City, USA : IEEE, 2006.
[4] S. Russel, P. Norvig. Artificial Intelligence: A Modern
Approach. 2003. pp 115-116