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A Practical Probabilistic Approach to Predicting Glycaemia Levels in
Intensive Care Unit Patients
David McDonagh, Michael G. Madden
College of Engineering and Informatics, National University of Ireland, Galway
d.mcdonagh1@nuigalway.ie, michael.madden@nuigalway.ie
Abstract
We propose a practical approach to predicting
intensive care unit (ICU) patient plasma glucose levels
so as to combat the occurrence of hyperglycaemia or
hypoglycaemia. A dynamic Bayesian network (DBN) is
designed for this purpose due to their ability to model
probabilistic events over time. DBNs allow for the use
of observed evidence to affect the probabilities of each
possible outcome. Physicians have indicated that
qualitative information is preferred to quantitative,
leading to the design of a discrete model. The discrete
DBN is compiled from the different variables that affect
plasma glucose levels, as well as additional variables
suggested by physicians.
1. Introduction
Glycaemia is the term used to describe the presence
of glucose in the blood. Hyperglycaemia is a condition
where there is an excess amount of plasma glucose. It
can lead to nausea, vomiting and death in extreme cases.
In ICU conditions, stress-induced hyperglycaemia in
non-diabetics is a common occurrence [1].
Hypoglycaemia is the opposite condition, where
there is a shortage of plasma glucose. Brain metabolism
is dependent on a continuous supply of glucose in order
for the brain to function correctly. Low plasma glucose
levels can cause seizures, coma, permanent brain
damage and death [2].
2. Problems with Blood Glucose
Measurement in the Intensive Care Unit
In ICU conditions, high glucose levels are treated
with the infusion of insulin and low glucose levels are
combated by glucose infusion. Plasma glucose levels
are measured manually and infrequently, from 1 hour to
12 hour intervals, depending on the patient’s stability.
Instrumentation errors can distort the true level of
plasma glucose during these measurements. For these
reasons, a model must be designed that can successfully
predict true patient plasma glucose levels [3].
Each patient is unique and as such, each patient may
react differently to insulin or glucose infusions. This
inter-patient variability means that the model required
should be able to be tailored to each distinct patient [3].
3. Dynamic Bayesian Networks
A Bayesian network (BN) is a graphical
representation of all of the variables within a particular
scenario and how they interact with one another. Each
variable is represented by a node and each node has its
own set of values. Each interaction is represented by an
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arc. Associated with each node is a conditional
probability table (CPT), which lists the conditional
probabilities of the occurrence of each of the node’s
values as a result of the values of its parent node(s) [3].
A dynamic Bayesian network (DBN) is a model that
predicts events over time. It is composed of BNs in a
series of time steps. The length of a time step can vary
to any consistent value. The state of a variable at one
time step can be affected by the state of a variable in the
previous time step. An arc is also used to represent this
interaction [3].
4. The Discrete Model
Our research group has been working on a
continuous DBN for the prediction of glycaemia levels.
Values in this model are purely numerical. Physicians
have suggested that a descriptive model rather than a
numerical model would be preferred. This necessitates
the design of a discrete DBN with each node value
representing a range of values. As such, example values
for nodes could be High, Normal and Low. For
example, taking the variable Plasma_Glucose, a result
of High would suggest that the patient could be
hyperglycaemic, Normal would suggest that plasma
glucose levels are regular and Low would suggest that
the patient could be hypoglycaemic.
5. Future Work
True patient data and literature knowledge will be
used to define the limits of each node value and will
also be used to define the conditional probabilities in
each node’s CPT. A physician will be asked to provide
expert knowledge and to suggest any improvements.
Results of the discrete DBN will be compared to the
true patient data in order to analyse performance.
6. References
[1] Kavanagh, B. P. and McCowen, K. C., 2010, “Glycemic
Control in the ICU”, New England Journal of Medicine,
363(26), pp. 2540-2546.
[2] Krinsley, J. S. and Grover, A., (2007) “Severe
hypoglycemia in critically ill patients: Risk factors and
outcomes”, Crit Care Med, 35(10), pp. 2262-2267.
[3] Enright , C. G., Madden, M. G., Russell, S., Aleks, N.,
Manley, G., Laffey, J., Harte, B., Mulvey, A. & Madden, N.,
2010, “Modelling Glycaemia in ICU Patients: A Dynamic
Bayesian Network Approach”, Proceedings of BIOSIGNALS-
2010, Part of the 3 rd International Joint Conference on
Biomedical Engineering Systems and Technologies, Valencia,
Spain.
[4] Russell, S. and Norvig, P., 2003, Artificial Intelligence: A
Modern Approach, 2 nd ed., Pearson Education, Inc., Upper
Saddle River, New Jersey, USA, pp. 492-568.