DIAGNOSIS OF DEMENTIA AND ITS PATHOLOGIES USING

BAYESIAN BELIEF NETWORKS

Julie Cowie, Lloyd Oteniya

Dept. of Computing & Mathematics, University of Stirling, Stirling, FK9 4LA, UK.

Richard Coles

Kildean Hospital, Stirling, FK8 1RW, UK.

Keywords: Bayesian Belief Networks, Dementia Diagnosis.

Abstract: The use of artificial intelligence techniques in medical decision support systems is becoming more

commonplace. By incorporating a method to represent expert knowledge, such systems can aid the user in

aspects such as disease diagnosis and treatment planning. This paper reports on the first part of a project

addressing the diagnosis of individuals with dementia. We discuss two systems: DemNet and PathNet;

developed to aid accurate diagnosis both of the presence of dementia, and the pathology of the disease.

1 INTRODUCTION

The assessment and correct management of

individuals with dementia is complex, and requires a

high level of expertise. The first stage of our

research has focused on the development of a

disease diagnosis system. The system can be viewed

as two parts: DemNet which aids a clinical decision

maker in diagnosing the likelihood of dementia; and

PathNet which provides a dementia consultant with

an indication of the possible disease(s) causing the

dementia. Both systems utilise the technique of

Bayesian Belief Networks (BBNs) to provide

probabilistic information pertaining to the presence

of dementia and likely pathologies. The BBN

formalism offers a natural way to represent the

uncertainties involved in medicine when dealing

with diagnosis, treatment selection, planning, and

prediction of prognosis (Gill et al., 2005). This is

due to the fact that the influences and probabilistic

relations among variables can be described readily in

a BBN, alternatively thought of in terms of cause-

effect relationships. Another attractive feature of the

formalism is that any probabilistic statement (often

causal) can be computed, where the user may wish

to query either a single variable, or more commonly,

combinations of variables. Further advantages of

using BBNs over other techniques are the flexibility

of the networks in the amount of information they

receive and their transparency in detailing why a

particular decision was reached. Techniques such as

decision trees can be difficult to use when only

partial information is available and other techniques

such as Artificial Neural Networks (ANNs) fail in

providing a sufficiently good explanation facility,

making it difficult for users to determine factors that

have contributed to the decision.

In this paper, we begin by providing the reader

with a brief description of the dementia syndrome,

common pathologies, and current ways in which the

disease is diagnosed. The two prototype systems

developed, DemNet and PathNet are then discussed

in detail. The paper concludes with a report on

preliminary findings and the potential for future

work.

2 DEMENTIA

2.1 About Dementia

Dementia is a term used to describe a syndrome

caused by a variety of brain disorders that have in

common a loss of brain function, which is usually

progressive and eventually severe (Alzheimer’s

Society, 2005). Symptoms of dementia may

291

Cowie J., Oteniya L. and Coles R. (2006).

DIAGNOSIS OF DEMENTIA AND ITS PATHOLOGIES USING BAYESIAN BELIEF NETWORKS.

In Proceedings of the Eighth International Conference on Enterprise Information Systems - AIDSS, pages 291-295

DOI: 10.5220/0002441802910295

 SciTePress

include: loss of memory, confusion and problems

with language, executive function, and social

function. The clinical decision support system

(CDSS) we have developed focuses on providing

support in diagnosing dementia and distinguishing

between the four most common dementias of old

age: Alzheimer’s disease (AD), Vascular dementia

(VaD), Dementia with Lewy bodies (DLB), and

Fronto-temporal dementia (FTD).

The current method adopted for diagnosing

dementia involves evaluation of the clinical

syndrome based on history and examination,

supported by screening investigations, and, where

appropriate, additional more specialised

investigations. In progressive degenerative

dementias, during the very early stages cognitive

deficits are usually subtle and their manifestations,

though representing change from pre-morbid

function in an individual, may remain within the

normal range for the general population. This

presents considerable challenges to early diagnosis.

However, early diagnosis and an understanding of

the underlying pathologies is of value in planning

treatment and, in some cases, initiating specific drug

intervention.

Systems to aid in medical decision making, and

in particular, disease diagnosis were introduced in

the medical field over 25 years ago. Despite their

potential usefulness in helping to provide early and

accurate diagnosis, relatively few are in general use

(Kaplan, 2001). This can be attributed to the

difficulties of integrating such systems into a clinical

setting which can generally be described as complex

systems consisting of involved algorithms,

procedures, and protocols. By addressing this

complexity and our continued interaction with those

who would potentially use the clinical decision

support system (CDSS), we hope to ensure our

system is adopted and is found a useful aid in the

diagnosis procedure.

2.2 Disease Diagnosis Systems

Many different operational research and artificial

intelligence techniques have been adopted by

CDSSs. Such techniques include the use of

mathematical models (Werner and Fogarty, 2001),

neural networks (Dybowski et al., 1996) and more

recently, optimisation techniques (De Toro et al.,

2003). Further details of the use of such techniques

are detailed in Oteniya et al, 2005.

CDSSs have also been developed that address

the specific area of dementia diagnosis. García-

Pérez et al. (1998) use data mining and neural

network techniques and Mani et al (1997) apply

decision-trees and rule-based approaches to

differentiate between Alzheimer’s disease and

Vascular dementia.

Our CDSSs extend such systems by providing a

means of identifying the presence of dementia and

the likelihood of underlying pathologies. In addition,

by using BBNs, our systems overcome some of the

difficulties other data mining techniques can present

(as detailed in Section 1).

3 DEMNET

DemNet uses a probabilistic model of dementia

diagnosis that incorporates patient history features

and physical findings. In an attempt to optimise user

friendliness and utility in a busy primary care

setting, the model seeks to use as numerically few

and as simple to use parameters as is consistent with

reasonably high diagnostic accuracy. DemNet is

described in terms of two components: the user

interface and underlying BBN. Each node of the

BBN relates to a question asked of the user. The

more answers to questions the user can provide, the

more accurate the system in diagnosing the presence

of dementia.

DemNet is designed to be used by clinical

practice nurses, who are involved in the primary

level assessment of patients.

In order to facilitate a hand crafted BBN,

information was elicited from our domain expert (a

practicing dementia consultant) via a number or

technical workshops. The process involved deciding

on key diagnostic variables and the relationships

between them, as well as quantifying the relations

probabilistically. In an attempt to optimise user

friendliness and utility in a busy primary care

setting, the models seeks to use as numerically few

and as simple to use variables as is consistent with

reasonably high diagnostic accuracy. This process

brought to light both advantages and disadvantages

of this technique. Further information on these, the

elicitation process and the methodology adopted is

given in Oteniya et al, 2006..

3.1 The Bayesian Belief Network

The underlying DemNet BBN is given in Figure 1.

In the model, the nodes on the periphery of the

network collect evidence relating to: the individual’s

current functioning, global severity of cognitive

impairment, individual’s age, duration of

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impairment, and whether clear progression is

identifiable.

Figure 1: DemNet Bayesian Belief Network.

With each piece of evidence presented, the model

recalculates the posterior probability of the related

child nodes. For example, in our model, the state of

node 4 (current functioning) combined with

evidence presented to nodes 3, 5, and 7 will cause

the recalculation of posterior probabilities for child

nodes 6 and 11. This implies that information given

regarding the level to which cognition is impaired

(node 3) and the degree to which subtle functioning

is effected (node 5), combined with the state of

current functioning (node 4), influences a patient’s

global severity (node 6) and ultimately the

likelihood of the patient having dementia (node 11).

The network also incorporates the level of influence

which data has on increasing a related node’s

probability. In Figure 1, we can see that a subset of

information has been provided about a given patient

(denoted by dark grey nodes). Given the

information provided, we can see that this patient

has a 69% chance of having dementia.

3.2 User Interface

An intuitive user interface guides the user through a

series of questions representative of each node in the

BBN. This interface is depicted in Figure 2. The

interface is divided into 3 areas, namely the model

pane, diagnostic pane, and results pane. The model

pane displays the underlying DemNet model which

can be helpful to the user in identifying which pieces

of data they have provided (denoted by dark grey

nodes) and some notion of how each piece of data

relates to the diagnosis given. The diagnostic pane

displays the question associated with a selected

diagnostic node. Each node has a number of possible

states. Each of these states relate to possible answers

to the diagnostic questions. The user is therefore

restricted in the response they give by choosing the

appropriate answer from a drop down menu. The

results pane for DemNet shows 3 sets of results

relating to current functioning, global severity, and

dementia. The first two sets of bars in the results

pane relate to nodes in the model that are crucial to

determining whether dementia is present, and are

nodes which rely heavily on the answers/evidence

given to other nodes. The three bars associated with

Current Functioning and the three bars associated

with Global Severity are representative of the 3

states possible for these nodes. The dementia bar

indicates the likelihood of the disease being present.

Figure 2: DemNet User Interface.

4 PATHNET

PathNet is an extension of DemNet for use by

domain experts (usually dementia consultants)

within the primary care setting. The system allows

the decision maker to identify the underlying

pathologies of a given case of dementia. A number

of different pathologies can lead to the syndrome of

dementia, either singly, or in combination. It is the

four most common dementias of later life:

Alzheimer’s disease, Vascular dementia, dementia

with Lewy bodies and Fronto-temporal dementia

that PathNet aims to identify.

As with DemNet, the system can be divided into

the user interface and underlying BBN. As the

dementia syndrome may be caused by multiple co-

existing pathologies, we have designed our model

such that it is capable of identifying the likelihood of

different types of dementia, either singularly, or, as

is often the case, co-existing with other dementia

DIAGNOSIS OF DEMENTIA AND ITS PATHOLOGIES USING BAYESIAN BELIEF NETWORKS

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diseases. In addition, if dementia is suspected, but

none of the four most common pathologies can be

identified, the system indicates the presence of

dementia (other), that is, a form of dementia not

identified by the system.

Figure 3: PathNet results pane.

The PathNet interface is identical in layout to the

DemNet Interface but has one main difference in the

results pane. This incorporates information relating

to the likelihood of the different dementia

pathologies. This is shown in Figure 3. As can be

seen from Figure 3, the user is provided with a

visual representation of the probability distribution

across each of the diseases.

5 CURRENT FINDINGS AND

FUTURE WORK

5.1 Evaluation of Prototypes

To date, validation of the systems has involved a

workshop with expert physicians and informal

discussions with expert clinicians in the field. The

main purpose of the workshop was to discuss the

structure of the model, that is the variables and their

relations, and secondly, to discuss the validity of the

diagnostic output of the model for a number of

trivial and non-trivial typical scenarios. Recently,

we have initiated a small clinical trial which seeks to

collect clinical data relating to the diagnosis of

dementia and the pathologies. Once the data

collection study is complete (anticipated to be

October 2006), we will analyse the data using a tool

currently being developed which automatically

learns the structure and parameters from a given

dataset (see Section 5.2). This new model will be

compared with the hand-crafted models developed

to date.

5.2 Inducing the Network

Current work is focused on collecting data from

clinical practitioners conducting dementia diagnosis.

We hope to build up a sufficient body of data to be

able to induce a BBN, and ultimately compare the

structure and diagnostic accuracy of the network

derived with original hand-crafted models, allowing

insight into the potential advantages and

disadvantages of each approach for building BBNs.

6 CONCLUDING COMMENTS

Although this research is still in its preliminary

stages, we feel confident that the systems developed

have great potential in aiding in dementia diagnosis.

Through presentations and informal discussions, we

have been able to gain some initial feedback from

practitioners involved in this area. This feedback

has been extremely enthusiastic and encouraging.

Over the next few months we hope to be able to

produce some results from the comparison with the

data-derived BBN and the expert-derived BBN.

These results will hopefully give useful indication of

the benefits of each approach as well as provide

insight for the direction of future research. Further

details of the project can be found on the project

website: http://www.cs.stir.ac.uk/~lot/dotpi/

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