1

Holonic and Optimal Medical Decision Making Under Uncertainty

Israa Al-Qaysi1, Zied Othman

2, Rainer Unland

1, Claus Weihs3, and Cherif Branki 4

1 Institute for Computer Science and Business Information Systems, University of Duisburg-Essen

45117 Essen, Germany

{israa.al-qaysi, rainer.unland}@icb.uni-due.de 2 Al-Mustansiriyah University, Department of Computer Science

Baghdad, Iraq

zied_othman @yahoo.com

3 Dortmund University, Department of Statistics

44221 Dortmund, Germany

weihs@statistik.uni-dortmund.de

4 University of the West of Scotland, Paisley Scotland

Cherif.Branki@uws.ac.uk

Abstract—Holonic multi agent medical diagnosis system

combines the advantages of the holonic paradigm, multi

agent system technology, and swarm intelligence in

order to realize a highly reliable, adaptive, scalable,

flexible, and robust Internet based diagnosis system for

diseases.

This paper concentrate on the decision process within

our system and will present our ideas, which are based

on decision theory, and here, especially, on Bayesian

probability since, among others, uncertainty is inherent

feature of a medical diagnosis process. The presented

approach focuses on reaching the optimal medical

diagnosis with the minimum risk under the given

uncertainty. Additional factors that play an important

role are the required time for the decision process and

the produced costs.

Keywords: Decision making, bayes' theorem, uncertainty,holonic

multi agent system, medical diagnosis, swarms intelligence.

I. INTRODUCTION

The purpose of the study is to employ decision theory and

bayes theory together to serve the medical diagnostic

decision through a simple system with smart technique.

Smart technique is taken from swam intelligent method. The

paradigm of swarm intelligence implies a set of

comparatively simple entities and produces sophisticated

and highly reliable results. In particular, the symptoms that

the patient has could be a symbol of any one of a number of

illnesses or may be no illness at all. Uncertainty is a center

element of modern medicine where diagnosis is the

classification of medical knowledge in disease categories.

From here, there was a need to address this hypothesis for

the purpose of raising the accuracy of optimal decision

making.

The rest of the paper is organized as follows: Section 2

describes the state of the art; Section 3 provides basic

concepts like explaining holonic multi agent system, process

of medical diagnosis with Self-organization and Emergence;

Section 4 presents optimal decision under uncertainties then

outlines a holonic as a moderated of autonomous agent for

Section 5; Section 6 describes optimal system; Finally,

section 6 concludes the paper.

II. LITERATURE REVIEWS

Medical practitioners encounter several difficulties

throughout the ability of diagnosis decision making for

patients. The core of medical problems is the diagnosis

approach. Many medical diagnosis systems are proposed in

the medical domain.

Clinical decision support system was proposed by Catley

([Catley 04], and [Catley 04a], [Yang 04]) to diagnose

babies in neonatal intensive care unit and assess the

effectiveness of real-time decision support. CDSSs using the

eXtended Markup Language (XML) are subsequently

offered as a secure web service.

Like the previous systems that work in isolation, it cannot

overcome the potential of diagnosis for complex process.

Some of these drawbacks have been solved with expert

systems. Expert systems can be applied in various areas of

medical domains like diagnostic assistance, agents for

information retrieval, expert laboratory information systems,

and system for generating alerts and reminders...etc.

DXplain ([Barnett 87], [Barnett 96], [Elhanan 96], [Banu 8]

and [Feldman 91]) is a Clinical decision support expert

system that can be used as a medical reference system, as a

diagnostic clinical decision support tool, and as an

electronic medical textbook.

2010 IEEE EMBS Conference on Biomedical Engineering & Sciences (IECBES 2010), Kuala Lumpur, Malaysia, 30th November - 2nd December 2010.

978-1-4244-7600-8/10/$26.00 ©2010 IEEE 295

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Milho [Milho 00] has presented a web supported

development tool that is specific for medical diagnosis,

which is based on Bayesian networks. Medical diagnosis

needs more aspects like increased autonomy in process,

capability of communication, and cooperation with other

systems. In fact, agents contain these proprieties.

Multi Agent Systems ([Yang 08], [Shieh 08]) have been

proposed in several different kinds of problems in the health

care area. A multi agent diagnosis helping system

(MADHS) is considered as cooperative agents in medical

diagnosis. Coordinator, Joint Decision Maker, Examiners,

and Specialists are the types of the agent classes that

appeared in MADHS.

Holonic Multi Agent System (HMAS) is much more

selforganizing and flexible in view of the fact that the

holons are self-reliant units that have a degree of autonomy

to make its own decision on their exacting level of existence

without asking higher-level holons for support.

Diagnostic problems need high quality aspects to solve in

the health care. ([Ulieru 00, 02, 03, 03a, 03b, 06], [Unland

05]) suggest the proposal of Internet-enabled Soft

Computing Holarchies for e-Health Applications.

Holonic medical diagnosis system has been organized

according to the principles of swarm intelligence. This

system combines the advantages of the holonic paradigm

multi agent system technology, neural networks, and swarm

intelligence in order to realize a highly reliable, adaptive,

scalable, flexible, and robust Internet-based diagnosis

system for diseases.

III. BASIC CONCEPTS

A. Holonic multi agent system (HMAS)

Autonomy, sociality, reactivity, adaptability, and proactivity

are properties of multi agent system, while agent is an

autonomous entity, which observes and acts upon an

environment. Each node of the system should be able to

initiate its tasks and actions based on what it learned via its

interaction with other nodes to solve complex problems.

Agents can be competent of flexibility and autonomy in

environment to convene its design objectives.

A holon is artificial design, which is coherent and stable. In

this sense, holonic multi agent system consists of several

holons (sub-structures).

In fact, We can classify the core aspects of Holonic multi

agent system into two direction, on one hand, Holonic multi

agent system are robustness (the system capable of coping

in a suitable way with variations with the nature of the

surrounding environment) and soundness (inference rules

prove only formulas, which is valid with respect to its

semantics). On the other hand, Agents in this kind of system

facilitate the integration of heterogeneous systems.

B. Medical Diagnosis process

Medical diagnosis is the process of identifying a medical

state or disease by its symptoms, signs, and results of

different diagnostic procedures. Diagnosis is a very complex

process, there is the comparison of signs, and symptoms to

the “normal disease” posited. The physician will formulate a

hypothesis of probable diagnoses, and, in many cases, will

obtain further testing to confirm or clarify the diagnosis

before providing treatment; in other word, Identification of

disease and treatment proposal based on provided patient’s

data. The process that may continue in a number of

iterations in anticipation of the patient is finally diagnosed

with enough certainty and the cause of the symptoms is

recognized [Kappen 02].

The use of computer programs in the diagnostic procedure

has been a protracted term goal of research in information

technology, and definitely, it is the most typical application

of artificial intelligence and may be one of the particular

characteristics of both medical knowledge and the

diagnostic task. An accurate diagnosis will, in most cases,

lead to suitable treatment.

C. Self-organization and Emergence

Any system described as self-organizing is able to

determine its inner structure by itself as the environment

changes. Holonic self-organization combines one of the

important aspects of hierarchical organizational structures

by clustering the entities of the system into nested

hierarchies [Dilts91].

The perception of self-organization is conflated with that of

the correlated concept of emergence. Emergence is a

fundamental attribute of process; it would be difficult and

complex without the process of emergence. From other side

the emergent behavior is also hard because the number of

communications between each of the components increases

combinatory of a system with the new components and

subtle types of behavior to emerge.

IV. OPTIMAL DECISION UNDER UNCERTAINTIES

Decision theory is an organization of knowledge and related

analytical techniques of different degrees of procedure,

designed to assist a decision maker chosen among a set of

alternatives in light of their possible consequences

[Frederick 05]. Decision theory can be relevant to

conditions of certainty, risk, or uncertainty and seeks to find

strategies which maximize the expected value of a utility

function which measures the desirability.

Bayesian decision making is making a decision about the

state of nature based on how probable that state is [Charles

04].

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General equation of Bayes' rule for mutually exclusive

diseases and for symptoms complex jSSS ,...,, 21 which is

to be included for assessment:

( ) ( )

∑

=

=

k

jjDPjDjSSSP

iDjSSSPiDPjSSSiDP

1

)()),...2,1((

),...2,1(),...2,1((

If it is assumed that, the Di's are mutually exclusive, then:

∑

=

=

k

jjDPjDjSSSPjSSSP

1

)()),...2,1((),...2,1(

Equation (1) turns into the following form:

( ) ( ).

1

)()),...2,1((

),...2,1(),...2,1((

∑

=

=

k

jjDPjDjSSSP

iDjSSSPiDP

jSSSiDP

But if and only if the symptoms S , S ,..., S j are

independent from each other, then:

( )( ) ( ) ( )

( ) ( ) ( )∑

=

=

k

iiDjSPiDSPiDP

iDjSPiDSPiDP

jSSSiDP

1

/.../1

/.../1,...2,1/

The complete Bayes' formula stands for the complex

independent symptoms jSSS ,...,, 21 and the set of

mutually exclusive diseases KDDD ,...,, 21 .In other

words, if we use eqn. (2), then we have two assumptions:

1- Symptoms are independent from each other.

2- Diseases are mutually exclusive.

If the symptoms independence cannot be assumed, then

conditional probability will be given by the following eqn.

Then we complete Bayes' formula for the complex

dependent symptoms jSSS ,...,, 21 and mutually exclusive

diseases KDDD ,...,, 21 :

In other words, if we use (4), we just have one assumption

that diseases are mutually exclusive.

V. A HOLONIC AS A MODERATED OF AUTONOMOUS AGENT

Autonomy refers to the principle that agents manage

without the intervention of external elements. In particular,

agents decide for themselves whether or not to perform an

action.

Sub-holons always have the same organization as the

superholon. Agent team forming a holon and acts as a single

entity in its environment. [Christian 99] proposed three

different structures for holonic multi-agent systems:

Federation of autonomous agents, Moderated Group and

Fusion

Figure 1: Autonomy of the agents

In this article we have adopted moderated group as

management structure. Moreover, higher-level holons can

deliberately reduce the autonomy of their subordinated

lower-level holons. Holons are self-reliant units that have a

degree of autonomy to make its own decision on their

exacting level of existence without asking higher-level

holons for support. In fact, an open issue of HMAS is to

give holons means of self-organization to satisfy their goals.

A Holon is a self-similar structure composed of holons as

substructures. This hierarchical structure composed of

holons is called a holarchy. It has two aspects, from one

side; Holon is a whole-part construct that is composed of

other holons, excluding it is, at the same time, a component

of a higher level Holon. A more detailed description can be

found in [Koestler 07].

VI. FLEXIBLE ROLES IN A HOLONIC MULTI AGENT SYSTEM

Holonic concept grouped in five sets in order to simplify the

design of a multi agent society: the first one is belong to the

hierarchical structure of a holonic system; the second one is

relative to necessary equilibrium between properties of

cooperation and autonomy of holons; the third one is

relative to communication between holons; the fourth one

describe activities and control mechanisms; the last one

presents adaptation of holonic system to their environment.

Holonic formal and autonomous agents are defined in term

of goals. Whereas, social behavior involves an agent

interacting with others. In fact, to do so, it must model them,

their interaction, and their plans.

world= (environment, mas)

multi agent system

( ) ( ) ( )12121312121 ,...,,)....,,(),(),...,(−

= niniiiij SSSDSPSSDSPSDSPDSPDSSSP

( )( ) ( ) ( )

( ) ( ) ( )∑=

−

−

=k

i

niniiii

niniiii

ji

SSSDSPSSDSPSDSPDSPDP

SSSDSPSSDSPSDSPDSPDPSSSDP

1

121213121

121213121

21

,...,,)....,,(),(

,...,,)....,,(),(,...,/

(1)

(2)

(3)

(4)

297

4

environment: E= { object0, object1,..., objectn }

object0 = ( name0 , states0 , properties0 , relations0 )

agent = ( states , knowledge , messages , behaviour.rules )

multi agent system environment is a tuple: (A,e ,P, D)

Where:

A= { nααα ,...,, 21 } is set of all agent

Each agent iα is the tuple ( iS , iP , iA , iQ )

ε is set of environmental states

(→Π ii PP ×× ... ) is perception function

(×∆ ε: nAA ...1 ) ε→ is an environment function

Merging a multi agent environment into a single agent

environment:

E= ( { nααα ,...,, 21 } ε, , Π , )∆

Where: iα the holonic merge of ( mii .1, ,...,αα )

New entity appears in the system when set of holons merge

into super-holon. When the holon is not satisfied with its

current super-holon it has two possibilities. The first is to

quit its super-holon entirely and attempt to find a new holon

to merge and collaborate with. The second is to try to merge

with a second super-holon while remaining as a member of

the first super-holon.

VII. OPTIMAL DEAGNOSIS SYSTEM

The process of finding the best medical diagnosis from

slight information from the user is a great challenge,

whereas, Medical Diagnosis Decision Support HMAS under

uncertainty is one of the first systems that capable to reach

this purpose. A large number of simple Agents, Swam

Intelligent, Holonic paradigm, Bayes' theory, and Decision

theory are elements required in specific functions to perform

the diagnostic process. Possession of the diagnosis starts

from the mediator agent, which is in the highest level of the

hierarchy. Mediator agent is the importing point to the

system that is capable to accept or reject the request

according to its knowledge about the whole system and

class.

Fig.2 illustrates retrieved optimal decision starting from

local to global optimal decision. Leaf agent returns the

request (local optimal decision) on the form of diagnostic

decision added to total risk. Highest level will receive the

diagnostic decision with its total risk from multiple

branches; afterward, it selects the local optimal decision that

has minimum total risk. Finally, the global optimal decision

will be selected from the agent mediator that received from

a huge number of agents and from different disciplines.

Figure 2. Retrieve optimal decision

leaf agent takes its decision according to the Decision's

Pyramid steps, but the higher level takes its decision

according to selecting the minimum total risk that is sent

from the previous level.

set of state set of percepts

set of actions

set of agent function

iiii ASPS ×→×

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Process of finding the intelligent medical diagnosis from

raw symptoms is also a great challenge; whereas, Medical

Diagnosis Decision Support HMAS under Uncertainty is

one of the first systems that capable to reach this purpose.

Simple Agents, Swam Intelligent, Holonic paradigm, Bayes'

theory, and Decision theory are elements required in

specific functions to perform the diagnostic process. Fig. 1

illustrates the architecture of the holonic multi agent system

based decision in a three level where mediator agent is the

first one. Level two represents Disease Specialist Agents.

That, in turn, plays the role as internal nodes of the

hierarchy. The leaf level, In turn, is Disease Representative

Agents. They represent decision makers, which are

specialists on a particular domain of class diseases. Multi

simple agents communicated to pass through the blackboard

technique.

VIII. CONCLUSION

The presented approach focuses on finding optimal medical

diagnosis with the minimum total risk under uncertainty.

Primarily, uncertainty is often a core aspect of medicine in

general. It comes from the restrictions of medical

knowledge, which is a combination of scientifically precise

and clinical impressions leaving much space for medical

uncertainty. This paper as well handles an important

assumption for Bayesian theory and applies the rule without

assuming independence of symptoms.

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