Fuzzy Cognitive Map Hierarchical Triage Decision Support

for the Elderly

Voula C. Georgopoulos

and Chrysostomos D. Stylios

School of Health and Welfare Professions, TEI of Western Greece, Patras, Greece

Dept. of Informatics and Communications Technology, TEI of Epirus, Artas, Greece

Keywords: Medical Decision Support, Health System, Modelling, Soft computing.

Abstract: Fuzzy Cognitive Maps (FCMs) is a soft computing technique that has successfully been used to model

complex systems and to develop Medical Decision Support Systems for many medical discipline

applications. FCMs have a great ability to handle complexity, uncertainty and abstract inference as is the

case in the health care sector. In this work a Hierarchical structure is introduced within an integrated health

system where the Supervisor is modelled as an abstract FCM to support the triaging procedure. At the lower

level, the FCM-ESI DSS is used to estimate the Triage ESI level of every patient. This FCM-ESI DSS is

developed based on a novel approach, which ensures a high degree of inferring from human experts.

1 INTRODUCTION

The significant difficulty of making decisions in the

health care area of Emergency Department (ED)

service delivery is due to the inherent complexity

and intrinsic uncertainty of EDs and their dynamic

nature. Taking into consideration the aging

population increasingly seeking services at the ED in

combination with the limited resources and

increased costs lead to the need for development of

decision making tools that will effectively and

efficiently provide patient care in a timely fashion.

Emergency Departments (EDs) vary from

country to country both in terms of range of services

offered as well as patients arriving for care in EDs.

However, a recent definition provides the essence of

what goes on in EDs: “Emergency departments

provide unscheduled care for a wide variety of

persons for reasons that range from life-threatening

conditions to problems that could be treated in a

primary care setting” (Nawar et al., 2007).

Triaging involves an initial sorting of patients

who arrive at the emergency room, usually called

emergency department (ED), by rapidly identifying

patients requiring immediate care due to urgent, life-

threatening conditions as well as assessing the

severity of the problem so as to ensure that care is

appropriate and timely (ENA, 2001).

Patients are categorized according to the level of

urgency (Fernandes et al., 2004); (Travers et al.,

2002), based on their complaints, their general

condition, a brief examination and physiological

factors. In this way triage systems permit

minimizing of the waiting time for treatment of the

most urgent patients, while those not in need of

urgent treatment are placed in a waiting area.

Given the constantly increasing age of a

population this leads to increased numbers of visits

of elderly patients to the ED and as a result to an

increased burden on the EDs (Yim et al., 2009). The

elderly often have multiple and complex diseases

(Aminzadeh and Dalziel, 2002) and, as a general

rule, undergo more diagnostic testing and have

longer length of stays than younger patients. The

elderly frequently visit the ED because of their

increased prevalence to chronic-degenerative

diseases, susceptible to frequent exacerbations.

It is significant to mention that in study of a

sample of 50 randomly selected cases of ED

admissions patients 65 years or older, discrepancies

were found between the medical staff and expert

nurses in 20 cases: where staff nurses had

undertriaged 13 patients and overtriaged 7 patients

(McCall, et al.,2009). According to another study

(Grossmann et al., 2012) of patients over 65,

undertriage occurred in 22.5% cases. Main reasons

were neglect of high-risk situations and failure to

appropriately interpret vital signs.

Since all patients presenting to the ED are not of

equal severity and complexity, those that do not

577

C. Georgopoulos V. and D. Stylios C..

Fuzzy Cognitive Map Hierarchical Triage Decision Support for the Elderly .

DOI: 10.5220/0004634105770583

In Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (HA-2013), pages

577-583

ISBN: 978-989-8565-69-3

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

have a severe/and or life threatening condition will

have to wait to receive care. For the elderly

population where the complexity of problems is

increased, a long wait may cause deterioration of

their condition. Therefore it is important that

patients are also prioritized after the triage

classification within their classification category and

not be tended to on a first-come first-served basis.

Emergency rooms are extremely complex not

only in the patient and treatment protocols, but also

due to the high level of automation and

instrumentation, huge volume of information, and

interdisciplinary coordination that is necessary

(Christian et al., 2006). As such the triage decision

can be modelled using soft computing modelling

techniques such as Fuzzy Cognitive Maps discussed

in the next section.

In this paper, a two-level Decision Support

System is discussed to automatically assist in the

triage classification as well as assign and update

priority for patients which must wait according to

their classification.

2 FUZZY COGNITIVE MAPS

Fuzzy Cognitive Maps with their modifications

integrate aspects of fuzzy logic, neural networks,

semantic networks, expert systems and they are

usually supplemented by other soft and hard

computing methodologies. An FCM is illustrated as

a causal graphical representation consisting of

interrelated concepts. FCMs are fuzzy signed

directed graphs permitting feedback, where the

weighted edge w

from causal concept C

to affected

concept C

describes the degree by which the first

concept influences the latter. FCMs are

characterized as fuzzy feedback models of causality,

where the weighted interconnections between

concepts of the FCMs present causality between

concepts by creating an interconnected network of

interrelated entities, like an abstract mental model.

Feedback interconnections are permitted along with

if- then inferencing; that permits FCMs to model

complex nonlinear dynamic systems. FCMs have the

ability to include hidden nonlinear dynamics.

The concepts of the Fuzzy Cognitive Model

stand for the main characteristics of an abstract

model of any system, each concept of the FCM

represents a granular entity such as state, variable,

input, output, event, action, goal, trend of the system

that is modeled as an FCM. The value of every

concept

is A

and it results from the

transformation of the fuzzy real value of the

system’s variable, for which this concept stands for,

in the interval [0,1]. This produces the initial

concept value which is then updated as it is

computed through the interaction of the

interconnected concepts with the corresponding

weight. Generally, between two concepts there are

three possible types of causal relationships that

express the type of influence from one concept to the

other. The weight of the arc between concept

and

concept

could be positive

)0( 

which

means that an increase in the value of concept

leads to the increase of the value of concept

, and

a decrease in the value of concept

leads to the

decrease of the value of concept

. When there is

negative causality

)0(



which means that an

increase in the value of concept

leads to the

decrease of the value of concept

and vice versa.

Finally, there can be no causality

)0( 

The value

of concept

expresses the

degree of its corresponding physical value. FCMs

are used to model the behavior of systems; during

the simulation step, the value

of a concept

calculated by computing the influence of the

interconnected concepts

C ’s on the specific

concept

following the calculation rule:

 











)wAf(AA

)(

(1)

where

)1( k

A is the value of concept

simulation step



)(k

is the value of concept

C at simulation step

w is the weight of the

interconnection from concept

C to concept

and

is the sigmoid threshold function:









(2)

where

0



is a parameter that determines its

steepness. In this approach, the value

1



has

been used. This function is selected since the values

of the concepts must lie in the interval [0,1].

Fuzzy Cognitive Maps have been used to

develop Medical Decision Support Systems

(MDSS). A specific type for Medical Diagnosis is

the Competitive Fuzzy Cognitive Map (CFCM)

(Georgopoulos et al., 2003); (Georgopoulos et al.,

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578

2005); (Georgopoulos and Stylios, 2008) which

consists of two main types of concepts: diagnosis-

concepts and factor-concepts. Figure 1 illustrates an

example CFCM model that is used to perform

medical diagnosis. Here, the concepts of the FCM

and the causal relations among them that influence

concepts and determine the value of diagnosis

concepts indicating the final diagnosis are

illustrated.

In the CFCM model each diagnosis concept

represents a single diagnosis, which means that these

concepts must be mutually exclusive because the

main intention is to always infer only one diagnosis.

This is the case of most medical applications, where,

according to symptoms, medical professionals

conclude to only one diagnosis and then decide

accordingly concerning the treatment. Actually, this

comes from the medical axiom: “every patient has

only one disease” but may represent many

symptoms related to different diseases but all are

results of the primitive disease. The general

diagnosis procedure is a complex process that has to

take under investigation a variety of interrelated

factors, symptoms and functions. In accomplishing

any diagnosis process, some of these factors are

complementary, others are similar and even others

are conflicting.

Figure 1: A CFCM model for Medical Diagnosis.

In the Competitive Fuzzy Cognitive Map model, the

factor-concepts can be considered as inputs into the

MDSS from patient data, observed symptoms,

patient records, experimental and laboratory tests

etc, which can be dynamically updated based on the

system interaction, whereas the decision-concepts

are considered as outputs where their estimated

values outline the possible diagnosis for the patient.

3 ESI TRIAGE SYSTEM

When a patient first arrives in the Emergency

Department, the first stop is triage where a trained

and experienced registered nurse typically prioritizes

each patient's condition into one of five general

categories. This is done according to the Emergency

Severity Index (ESI) which was designed for use in

ED triage by the US Department of Health &

Human Services. The ESI is a five-level

categorization algorithm that prioritizes patients into

five groups from 1 (most urgent) to 5 (least urgent)

on the basis of severity and the number of resources

that the patient may need to receive proper care

(Wuerz, 2001).

In particular, the ESI uses the following scale

based on decision points to determine its categories

(Gilboy et al., 2005); (Barbee et al., 2010):

 ESI category 1- Emergent: patient intubated,

without pulse or respiration, or unresponsive. i.e

the patient requires immediate life-saving

intervention so as to prevent loss of life, limb, or

eyesight,

 ESI category 2- Urgent: patient is in a high-risk

situation, or confused, lethargic or disoriented, or

in severe pain, or danger zone vital signs.

 ESI category 3- Acute: patient is in need of many

resources to be taken care of. These may include,

for example, Laboratory Tests, ECG, X-rays, CT-

MRI-ultrasound-angiography, IV fluids, specialty

consultation, com-plex procedures etc.

 ESI category 4- Routine: patient is in need of one

resource.

 ESI category 5- Non urgent: patient is in need of

no resources.

Due to the dynamic and uncertain nature of the

overall triage process in addition to the

differentiation difficulty, methods are needed to help

the triage nurse to be efficient in making

prioritization among the patients with the same

acuity classification. Triage is a dynamic process in

decision-making and the determination of who needs

the most immediate care must be reassessed as

contextual factors change and additional patient

information becomes available [Patel, et al., 2008].

The triaging procedure requires a continuously

monitoring and keeping track of patients waiting to

be seen and a prompt assessment of each new

patient who arrives at the triage area. All the above

mentioned factors have to be taken into

consideration and they contribute to the complexity

of decision-making and create a degree of

uncertainty for the triage procedure. Therefore a

Decision Support System on Fuzzy Cognitive Maps

FuzzyCognitiveMapHierarchicalTriageDecisionSupportfortheElderly

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for ESI Triage is developed in the next section.

4 FUZZY COGNITIVE MAPS

MODEL FOR THE 5-LEVEL

ESI TRIAGE SYSTEM

In an ED triage system each patient is assigned one

of the 5 ESI levels and therefore, the Fuzzy

Cognitive Map ESI will include 5 Decision

Concepts (DC) each one for every ESI level:

 DC1 ESI Level 1;

 DC2 ESI Level 2;

 DC3 ESI Level 3;

 DC4 ESI Level 4;

 DC5 ESI Level 5.

The FCM development procedure is based on

human experts who have to define the factor

concepts that are represented at the FCM. There is

group of experts who are asked to select the main

factors based on which they conclude to an ESI

triage; everyone expert replies with a set of 3-5

factors or even more sometimes.. The frequency

with which each factor was chosen by the group of

experts as a whole determined the importance

weight (

) between a factor concept and a

decision concept.

The 22 factor concepts (FC1-FC22) of the FCM

are and their

have been detailed reported in

(Georgopoulos and Stylios, 2012); (Georgopoulos

and Stylios, 2013) and are mentioned in Table 1.

Moreover, the experts are asked to evaluate the

triage stage of specific cases and based on their

assessment we infer additional information, which

leads to a complementary second weight, the

“influence to specific decision” specific weight-

, which represents how much the specific factor

leads towards a specific decision / diagnosis. The

procedure to calculate the

sw is the following:

every expert who considers one factor as important

and takes it into consideration, is asked to present

the degree with which this specific factor leads the

expert to select one decision. Every expert describes

the degree of influence of one factor towards one

decision using a linguistic variable, such as “very

strong influence

vsi”, “strong influence, si”,

“medium influence,

mi”, “weak influence wi”, “very

weak influence vwi”, as it is depicted at Figure 2.

Thus, every expert describes the specific weight

of each interconnection with a fuzzy linguistic

variable from the above mentioned set, which stands

for the relationship between the two concepts and

Table 1: Factors of the FCM.

FC1 Life threatening 0.45

FC2 Limb threatening 0.40

FC3 Patient chief complaint 0.67

FC4 Vital signs 0.4

FC5 Medical history 0.35

FC6 Other factor 0.32

FC7 Expected # of resources 0.31

FC8 Patient age 0.16

FC9 Required timely intervention 0.15

FC10 Weakness 0.20

FC11 Additional symptoms other than chief

complaint

0.14

FC12 Severe pain or distress 0.12

FC13 Patient referred to ED from outside 0.08

FC14 Behavioral or psychiatric issue 0.07

FC15 No additional symptoms to chief

complaint

0.05

FC16 Absence of medical history 0.05

FC17 Patient medications 0.05

FC18 Hospital or ED discharge<3 days 0.04

FC19 Patient immune-compromised 0.04

FC20 Alcohol or illicit drug use 0.03

FC21 No recent change mental state 0.75

FC22 Patient can walk or sit 0.12

Figure 2: The positive fuzzy linguistic weights.2

determines the grade of causality between the two

concepts. Then, all the proposed linguistic weights

for one interconnection suggested by experts, are

aggregated using the SUM method and an overall

linguistic weight is produced. The overall lingustic

weight with the defuzzification method of Center

Of Gravity (COG), is transformed to a numerical

weight , belonging to the interval [-1, 1].

Then, the overall weight describing the influence

from one factor concept towards a decision concept

is calculated using the form:





swliwlsww

**)sgn(



(4)

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where the two parameters are introduced to

represent the participation of the importance weight

and the specific weight, on the overall weight

describing the influence of every factor concept

towards the decision/diagnosis concept. It is

mentioned that the value of has to be normalized in

the interval [-1, 1], where the weight takes values.

The previously developed FCM-ESI model did

not provide for interactions between the various

factor concepts. However, because the decision is

very complex and there are always cause effect

relationships between factors contributing to the

triage decision, it is important to extend the previous

FCM-ESI DSS system to include such connections

between factor nodes.

In the current model these are as follows:

 Vital signs (FC4) and Patient chief complaint

(FC3) Vital signs (FC4) and Patient immuno-

compromised (FC19) – for example patients that

are experiencing fever and are on chemotherapy.

 Over the counter medications (F15) and chief

complaint (FC3) – patients using over the counter

pain medication may have decrease in their pain

level and as a result the severity indicated

concerning their chief complaint may be decreased

Figure 3: The FCM-ESI model for triaging.

 Over the counter medications (F15) and vital signs

(FC4) – over the counter medications may change

vital signs, e.g. reduced fever, increased blood

pressure etc

Experts were asked to identify the possible

interactions among Factor Concepts. At first, every

expert is asked to determine the pair of concepts that

are coupled. Thus, a set of possible paired factors

concepts is created and then all the experts are asked

to suggest the degree (using a linguistic weight) of

coupling/ influencing among the previously

identified pairs of factor concepts. A set of five

fuzzy linguistic values are used.

The complete FCM-ESI is illustrated in Figure 3

where the 5 central nodes are the decision nodes

(ESI levels). The decision node with the maximum

value is the level at which a patient is triaged.

The FCM-ESI for every patient takes the concept

factor values from measurements, laboratory test and

examination and these values are transformed in the

interval [0,1] where concepts take values and then

the values of Decision Concepts are calculated, so

that to infer the ESI level.

5 PRIORITY BETWEEN

EQUALLY TRIAGED

PATIENTS

FOR ESI LEVELS 3-5

An important issue after initial triage for patients

with ESI levels 3-5 in an overcrowded ED is the

priority with which patients receive care. Usually,

this is on a first come – first served basis. However,

this can change over time. For example, in some

cases injuries and illnesses that need medical and

nursing intervention are time sensitive. The longer

the wait, the more damage may occur because of

changes for example such in oxygen, blood,

electrolytes (potassium, sodium, etc.), sugar, etc.

Failure to prioritize triaged patients appropriately

may result in very sick patients at risk for

deterioration while waiting. Since this is also

difficult and critical decision for the personnel in the

ED, a supervisory level has been added on the

FCM-ESI DSS model, where the outcome is

changes in priority for patients within the same ESI

level.

In order to develop the supervisor priority FCM-

ESI Decision Support Systems, there are selected the

most essential factor concepts that may influence the

patient status. The supervisor priority FCM-ESI

consists of the concepts (Figure 4):

 Change in vital signs (FC4).

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 Patient report of worsening symptoms.

 Change in mental state (FC21).

 Change in patient can walk or sit (FC22).

 Triage ESI level.

 Time in waiting area.

Therefore, for each patient where a particular ESI

Decision Concept had the maximum value (i.e. the

ESI Level with which the patient was characterized)

this decision node interacts dynamically with other

FC nodes as new information is provided over time

and is checked every half hour.

This leads to prioritizing of the patients that have

equal or almost equal ESI status in order to avoid

adverse events after triage due to long wait in

overcrowding. Thus the supervisor priority FCM-

ESI is called to prioritize among patients with the

same ESI level.

6 CASE STUDY

A 72-year-old woman presented to a busy

emergency department (ED). During triage she told

the triage nurse that she experienced face and tongue

swelling in the last two days. The vital signs at

triage, including respiratory rate and oxygen

Figure 4: The supervisor priority FCM-ESI DSS.

saturation, were normal. No previous history

indicated this condition, the patient had not taken

medications, was not in any pain, and there were no

problems with the patient’s mental state, Also the

patient was able to sit and walk. Both a triage nurse

and the FCM-ESI resulted in ESI-Level 3.

The patient sat in the waiting room for more than

2 hours after which she was placed in a room in the

ED. After an additional hour a doctor evaluated her.

In the meantime, her tongue and throat had swollen

substantially, and she was having difficulty

breathing. She was diagnosed with angioedema and

required emergency intubation, a potentially

dangerous and high-risk procedure accompanied by

aggressive treatment with intravenous epinephrine.

On the other hand, using the supervisor priority

FCM-ESI DSS, when the patient after 1.5 hours

experienced substantial swelling and difficulty

breathing the triage the patient priority was

increased to Very-Very High and thus, the patient

received immediate care without the need for

intubation.

7 SUMMARY

This paper is an extension of previous work and it

presents an integrated methodology for developing a

hierarchical Decision Support System for ESI

Triage. Usually at the Emergency Department (ED)

of hospitals medical staff has to cope with many

patients, asking for urgent treatment and so they

have to assess their health condition under

significant time constrains. The case of elderly

patients has great importance as they usually are

admitted quite frequently at the ED suffering from

chronic problems, their health condition is

characterized by complementarity and/or

controversy and usually with a lack of interaction

and low level communication ability. Thus triaging

of elderly people is characterized by high

complexity and it makes a difficult task the

assessment and decision about health condition.

For such cases, Soft Computing methodologies

are rather suitable and so Fuzzy Cognitive Maps

(FCMs) are proposed here to model and develop a

Decision Support Systems for the ESI Triage, which

is a significant procedure during patient admission at

the Emergency Department (ED) of hospitals.

Generally, FCMs have been successfully applied to

develop Medical Decision Support Systems for

many discipline fields.

Here, there is further expansion of a recently

proposed methodology to develop FCMs exploiting

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and combining knowledge and experience of human

experts along with information and bibliographic

data. Moreover, a hierarchical two level structure is

introduced consisting of a FCM at each level. The

introduction of the abstract FCM supervisor

prioritizes among the different patients and increases

the efficiency of the MDSS.

ACKNOWLEDGEMENTS

This work was supported by the E.U. FP7–

PEOPLE–IAPP–2009, Grant Agreement No.

251589, Acronym: SAIL.

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