Optimizing Medical Data Quality Based on Multi-agent Web Service Framework

hundredcarriageSoftware and s/w Development

Nov 3, 2013 (4 years and 10 days ago)

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1



Abstract

One of the important issues of the e
-
healthcare
information systems is to precisely identify satisfied quality of the
medical data from a distributed and heterogeneous environment.
This paper proposes a multi
-
agent
web service
framew
ork based
on Service Oriented
Architecture (SOA) for the optimization of

medical data quality in the e
-
healthcare information system.
Based on the design of the multi
-
agent

web service

framework,
an evolutionary process for the dynamic optimization of the
med
ical data quality is proposed and supported with
experimental results
by building up
a case study.
In dealing with
complex and large
-
scale medical data requests, there is a
foreseeable bottleneck of supporting technology in medical data
selecti
on.
This
framework can provide the optimized
medical
data

quality
and dramatically reduce medical errors caused by
misjudgment of the e
-
healthcare Information System.


Index Terms

e
-
Healthcare, Medical Data Quality, Web
Service

I.

I
NTRODUCTION

It was reported that bet
ween 44,000 and 98,000 deaths
occur annually as a consequence of medical errors within
American hospitals alone [1] and the US National Association
of Boards of Pharmacy reports that as many as 7,000 deaths
occur in the US each year because of incorrect pr
escriptions
[2]. Therefore, a great desire to improve access to new
healthcare methods, and the challenge of delivering healthcare
becomes significant nowadays. In an attempt to meet these
great demands, health systems have increasingly looked at
deploying

information technology to scale resources, to reduce
queues, to avoid errors and to provide modern treatments into
remote communities.

Many medical information systems are proposed in the
literature trying to assist in management and advising medical
tre
atments to prevent from any type of medical errors. From
the individualized care point of view, in order for clinicians to
make the best diagnosis and decide on treatment all the
relevant health information of the patient needs to be available

and transpar
ently accessible to them regardless of the location
where it is stored. M
oreover, computer
-
aided tools

are
now

essential for interpreting patient
-
specific data in order to
determin
e the most suitable therapy fro
m the diagnosis, but






existing systems lack
collaborative ability because of
employing different method to design [3].

Many researchers have been trying to apply service oriented
architecture (SOA) to deal with the distributed environment
for e
-
healthcare information system [6]. The objective of SO
A
is to provide better quality service to users. The new service is
called web services [4]. Following the definitions and
specifications of web service, any organization, company, or
even individual developers who can deliver such functional
entities ca
n register and publish their service components to a
Universal Description, Discovery, and Integration (UDDI)
registry for public use. Web services can be as simple as a
single transaction, e.g. the querying of a medical record, or
more complex multi
-
serv
ices, e.g. supplying chain
management systems f
rom business to business (B2
B),

and

many other [4]. However, current web service developments
mostly focus on providing either a single service or at most a
few. Focusing on single service without being prepa
red for
complex and large
-
scale web services cause technological
bottlenecks to develop. Therefore, in order to enhance
service
-
oriented integration in distributed e
-
Healthcare
environment, the collecting and composing of web service
components for comple
x and large
-
scale web service
applications need to be developed and improved.

In composing web services, both a single service
component

and a series of service components that can
support large
-
scale tasks need to be found. Ko and Neches
also point out

that current web service research focuses only
on developing mechanisms to describe and locate individual
service components in a network environment [5].

In dynamic optimization of medical data quality, the
information regarding suitable medical data service
components need to be acquired from many medical data
service providers whose components are registered in a UDDI
registry repository. The next step i
s to negotiate with different
medical data service providers in order to integrate suitable
medical data components. The optimization of medical data

selection is successful when multi
-
objectives set by a medical
data service requester are met such as re
liability of medical
data

component,

results


of

diagnosis,

and

cycles

of




Optimizing

Medical Data Quality Based on

Multi
-
agent

Web Service Framework

Ching
-
Seh
Wu, Wei
-
Chun Chang, Sena Cebeci

and Ibrahim Khoury

Department of Computer Science and Engineering, Oakland University, USA

{cwu, chang234, secebeci, iskhoury
}@oakland.edu


2

consultation

[8]. To evaluate web service composition, several
aspects of the quality of service have been proposed, e.g. web
service composition


Business Process Execution Language
for Web Services (BPEL4WS) [7], web service coordination,

web service transaction, w
eb service security, and web service
reliability.

This
paper aims to apply the SOA of
Web Service concepts
specified above to put forward a model of multiple intelligent
agents based assistance in improvement of medical data
quality in the distributed e
-
Healthcare information system
environment which is able to optimize the medical data web
services according to data quality aspects. Furthermore, to
improve accuracy of doctor‟s diagnostic, many methods for
Medical Diagnostic and Treatment Advice Systems h
ave been
developed to assist medical doctors in decision making such as
rule based reasoning, fuzzy inference, neural network, and etc
[8, 9, 10]. Intelligent Agent is another approach taken by
researchers trying to assist in different domains such as
busi
ness process, remote education service, and project
management [11, 12, 13].
Our objectives of this research are
to design and develop medical data quality models and to
develop the methodologies and algorithms of our multi
-
agent
framework to assist in mon
itoring and optimizing data quality
for e
-
Healthcare information system.


In
the
following sections, we will first describe the
preliminary aspects of our study focusing on medical data
quality in terms of data extraction in section II. In section III,
sta
tic and dynamic behavior of medical data quality models
were designed and developed by using UML notations. These
models will be implemented for healthcare intelligent agents
to monitor and keep track of the medical data recording and
extraction process in

section IV. In section V, optimal medical
data quality framework in distributed medical data
environment is introduced and Evolutionary Computing is
used when optimizing the data selection. Case study for the
Breast Cancer disease is examined and indicate
d with
experimental results using the Evolutionary Algorithm in
section VI. Finally, Section VII concludes the paper.

II.

P
RELIMINARIES OF
M
EDICAL
D
ATA
Q
UALITY

Data quality refers to many different aspects. In Table 1,
aspects of the data quality were grouped
into two categories of
dimensions, measurable dimension and intangible dimension.
However, the main focus of the medical data quality in this
research has been on the measurable Accuracy of data quality
dimension. The accuracy of medical data in this study

refers to
the reality presentation of the medical data from data
extraction process during the healthcare governance cycle
specified in Figure 1. To receive an accurate set of medical
data for healthcare consultation, this study has designed
healthcare in
telligent agents to monitor and track the data
extraction process.

A.

Healthcare Governance Cycle

In order to design intelligent agent to monitor and to keep
track of medical data processing, the healthcare governance
cycle is illustrated in Figure 1. Within

the healthcare
consultation, the General Practitioner (GP), such as a family
doctor, uses a networked Healthcare Maintenance
Organizations (HMO) to find relevant healthcare knowledge
for the treatment.


Table 1.

Aspects of the Data Q
uality




Fig. 1.

Healthcare Consultation Governance Cycle

The healthcare data from each consultation will be stored in
medical database. The medical database records information
in a concise format with compressed detail clinical coding
regarding symptoms, diagnostic resul
ts, treatments,
prescriptions, and other medical information for the
consultation. When one of particular


medical

information


is

retrieved for further or next healthcare
consultation/reference, the compressed medical data must be
extracted to an understandable format for GPs. The feedback
on the medical data quality will be conducted to improve the
patient care for the next iterati
on of healthcare consultation.

The major concern of the medical data quality is drawn
from the data extraction process. One of the major challenges
in healthcare domain is the extraction of comprehensible
knowledge from medical diagnosis data. Data accurac
y and
consistency must be maintained during the extraction process.

In order to make sure that the data extraction process
maintains a good quality of medical up
-
to
-
date information,
medical data quality models are created for the further design
of health
care intelligent agents.


3

III.


M
ODELING THE
M
EDICAL
D
ATA
Q
UALITY USING
UML

A saying from Software Engineering said [14], “If you can
model it, you can implement it.” We have designed the class
diagram, the activity diagram, the use case diagram, and the
sequenc
e diagram for modeling the static view of the medical
data quality and the dynamic behavior of the medical data
extraction process using UML. By developing models, we are
able to look into the details of the medical data
recording/retrieval process as well

as the data extraction
process. This will help us design the multiple intelligent
agents to monitor and track the data recording/retrieval and
extraction processes to assist in medical data quality
improvement.

Use case diagram is tool for modeling the f
eatures and
functions of an information system. The Use Case Diagram in
Figure 2 shows that our system consists of medical data
quality features such as data extraction, data migration, data
cleaning, data integration, data processing and data analysis.
Da
ta analysis involves feedback and quality assessment. This
Use Case Diagram is the first step toward the definition of the
behavior of the medical data quality involved in a healthcare
information process. For this particular study, we only focus
on the da
ta extraction of the medical data quality. The rest of
medical data quality issues specified in Figure 2 has been
reserved for future study and development.




Fig.2
.

Medical Data Quality Modeling


Use Case Diagram


The class diagram of medica
l data qual
ity model in Figure 3

contains all classes/objects that associate with

medical data


processes and queries. Each class/object in the model was
used to generate data quality metrics/attributes for intelligent

agents to keep track and monitor.
When data
extraction
process is

conducted, the medical information in
classes/objects will be collected in medical knowledge base

for inference conducted

by intelligent agents.



Fig. 3. Medical Quality Modeling


Class Diagram


The activity diagram in Figure 4

models the activities and
tasks involved in the medical data extraction process. These
key activities including using hospital query language (HQL)
for hospital information system, medical data recording
process, and looping process for data update. The a
ctivity
diagram help
s to

design the internal monitoring process of
healthcare intelligent agents.

The sequence diagram o
f medical data model in Figure 5

shows the process sequence of medical data extraction. The
sequence diagram enhances the process defin
ition from the
activity diagram. Healthcare intelligent agent uses these two
models to identify quality items to be monitored.



4



Fig. 4.

Medical Data Quality Modeling


Activity Diagram




Fig. 5. Medical Data Quality Modeling


Sequence Diagram

IV.

O
PEN

D
ESIGN OF
I
NTELLIGENT
A
GENT
P
ROTOTYPE

Once both dynamic and static models for the medical data
quality have been designed, the intelligent agent was
developed according to the medical quality models to monitor
and to keep track of medical data recording an
d processing.
Our design of intelligent agent is an open and collaborative
infrastructure so that each agent has the same structure
enabling communications with each other. An agent, as
illustrated in Figure 6, consists of three service components,
collabo
ration service, quality monitor service, and reporting
service as described in Figure 8. The collaboration service
enables agents
to
plug & play medical web f
orms for portable
medical records

and to p
lug &
play data workflows, medical
protocols, and clinic
al g
uidelines

in a distributed
heterogeneous medical information environment, and enables
i
nformation exchange
service among intelligent agents. There
are two existing methods that can be used to implement
intelligent agents, Java Expert System Shell (JESS
) and Java
Agent DEvelopment Framework (JADE). The interior
implementation of services for an agent was carried out by
using JESS. The exterior communication behavior in a
distributed e
-
healthcare environment was carried out by using
JADE. In general, heal
thcare intelligent agents have been
developed to assist e
-
healthcare information system in medical
data quality improvement activities:



update medical knowledge base



define criteria for healthcare data query



determine if a threshold value o
f data quality
has



been reached



optimize data quality for the accurate diagnosis



keep track of patient

s healthcare profile



communicate with other agents





Fig. 6
. Open Module Design of an Intelligent Agent


JESS was used to develop and implement the interior
inference process of intelligent
agents as specified in Figure 7
.
The intelligent agent can take inputs from healthcare experts
and transfer the inputs into healthcare knowledge for inference
engine to ma
ke consultation judgments. Healthcare intelligent
agent was developed with the featur
e described in Figure 8
. It
is able to take inputs from medical events such as patient‟s
medical history, diagnosis knowledge from experts, and
medical symptoms. The inter
ior features include update
medical knowledge, define criteria for hospital queries,
determine if the data quality threshold value has been reached,
keep track of patient‟s medical profiles, and communicate
with other agents.



5



Fig. 7
. Intelligent Agent

Inference Structure



Fig. 8. A Healthcare Intelligent Agent

V.

O
PTIMIZATION OF
M
EDICAL
D
ATA
Q
UALITY

One of the most important concerns of this study is the
medical data selection for quality improvement over a
distributed e
-
healthcare information
environment. Th
e
foundation of satisfying
data quality over the distributed
medical data environment

compiles the
analysis and
construction

of
medical data
service workflow, the automation
of
composing
/optimizing

suitable
medical data
W
eb
S
ervice

component
s
, and
medical data
W
eb
S
ervice

component
reusability. To satisfy
data quality

criteria, we proposed a
framework (see Figure 9
) w
here we integrated intelligent
agent, a medical data repository

section and several modules
into the S
ervice
O
riented
A
rchitect
ure (SOA)
.


Evolutionary A
l
gorithms (EAs)
have been
appli
ed as the
searching algorithms to
search

the optimal
medical data in the
distributed e
-
healthcare information environment as specified
in Figure 9
. “Surviv
al of

the fittest”
[15]
is
a

principle in th
e
natural environment
which is

used in the
medical data
selection

algorithm to generate survivors
,

the

optimal
data
selection in the distributed healthcare environment
.

The

original

principles of
the
EC theory are based on
Darwin’s theory of natural
selecti
on to solve real world
problems
[
6
]. EAs have been successful
ly

applied
in
optimizing the solutions for a variety of domains [
6
]. The
strength of EC techniques comes from the stochastic strategy
of search operators.




Fig. 9
.

Optimizing Medica
l Data Quality F
ramework in distributed

Medical Data E
nvironment


The major components in EC are search operators acting on
a population of chromosomes. EC was developed to solve
complex problems, which were not easy to solve by existing
algorithms [
6
, 7
].

The method utilized in the algorithm to
progress the search from ancestors to offspring is the
collective learning process; species information is collected
during the evolutionary process, and the offspring that inherit
good genes from parents survive t
he competition. This is the
first characteristic of EAs.
Next, t
he generation of
descendants is handled by the search operators, crossover and
mutation; which explore variations in species information in
order to generate offspring. Crossover operators
exchange
information between mating partners. On the other hand, a
mutation operator, which mutates a single gene with very
small probability, is used to change the genetic material in an
individual. Finally, the third characteristic that defines EAs is
the evaluation scheme, which is used to decide who the
survivor is. The evaluation scheme is the most diverse
characteristic of the three due to the different objectives used
to select the different solutions needed in different domains.
The evaluation s
cheme can be as simple as good or bad, a
binary decision; or as complex as nonlinear
using

multiple
mathematical equations to assess trade
-
offs between multiple
objectives.

For this study

EC techniques provide
d

stochastic searching
techniques aimed at global optimization. Globa
l optimization
searches for the optimal
performance of solutions in the
objective space. A general global optimization pr
oblem can
be defined as follows:

)
x
(
f
min
)
x
(
f
x
*









)
(
x
c
to
subject


6

Where
f

(x)

is the global optimization in objective space
when determining the minimum of the function
f(x)
;
x

is a
vector of variables which lies in the feasible region


, any
x

in


defines a feasible solution in which
x

conform
s

to the
constraints
c(x)
. A similar definition can also be applied to the
maximization of objective functions.





Fig. 10
. Design of the Evolution Process of Medical Data S
election


The design objective of this study was to develop an EC
-
based process incorporated with a current web service
transaction procedure (see Figure 10) to search the optimal
medical data quality solution space. The space was created by
collecting information
of data service components through
UDDI registries for the optimization of medical data web
service composition. This type of evolutionary process has
also been developed and tested in requirements engineering in
order to search for the optimal quality so
lutions for system
specification [15].

The fundamental designs of an EC
-
based process in this
study were focused on the definition of medical data search
space, chromosome structure design, objective function
definitions, and quality fitness assessment al
gorithm. In
general, to apply the process in medical data web service
composition, the major steps of the process are defined as
follows:


1)

Collecting the medical data of component registrants:
the size of medical data searching space is decided by
the num
ber of component registrants collected from
available UDDI registries. Therefore, it is very
important to obtain the information of all available
medical data locations/components from component
registration agents. The information regarding the
descripti
on of service components can be collected
from a component library as specified in [16]. The
communication protocol is based on a set of API
message (i.e., UDDI 3.0 and up).

2)

Modeling medical data resources from different
providers: medical data service co
mponents are
classified and constructed into database tables based
on the functionalities and characteristics of medical
data service requested. The work flow of the medical
data service can be modeled by using a scenario
-
based method that is used in prev
ious sections to
describe the task steps required to accomplish the
completion of medical data web service
applications[15].

3)


Applying the sequence of medical data web service
composition and chromosome encoding/decoding:
the task sequence of medical dat
a web services that
are needed to be optimized is defined. A sub
-
task
service in a task sequence can be defined as:




j
ji
task
sub
component

,

w
here
it is

assumed that one sub
-
task can be
completed by a
medical data
service component. By
utilizing the
collect
ed
information of
medical data
component registrants, a web service task sequence is
transformed into a binary string, i.e. encoding a
quality
so
lution into a chromosome. The
chromosome mapping mechanism utilize
s

a

hie
rarchical structure [15] for
an

encoding/decoding


task sequence and chromosome.

4)

Quality Fitness Assessment:

To evaluate the quality
of medical data optimization, multi
-
parameters or
attributes are
used in the metrics to evaluate
performance and quality.



The metric measurement f
ocuses on different aspects
t
hat
data quality criteria require.
Such measurement is a
key element of evaluating the performance and quality of
medical data optimization
.

VI.

C
ASE
S
TUDY

In this section, we propose a case study for e
-
healthcare
subsequently us
ed throughout the paper. To demonstrate
Evolution Algorithm, Breast Cancer Web Service Task
Sequence is selected in this case study. The main goal of this
case study is to find the optimal solutions for diagnostics,
treatments and alternative treatments ac
cording to multi
-
objective medical data quality metrics. To indicate
the
efficiency of EA for optimizing data quality we implemented
the algorithm using MATLAB platform.

Figure 11 shows the web service task sequence of the
Breast Cancer. Following a phys
ical examination, wherein the
patient has been found to have Breast Cancer, the next step in
the sequence, will be choosing a series of tests that the patient
will undergo to provide treatment [19].

Table 2 shows the test, treatment and alternative treatme
nt
types in Breast Cancer. Based on test results, treatments and
alternative treatments are decided upon by the doctor.


Fig.11.

Breast Cancer Web Service Task Sequence



7

We focus on set of data quality dimensions which are
provided by intelligent agents,
namely: accuracy, consistency,
completeness and timeliness, which constitute the focus of the

majority of authors [17, 18].


Table 2 Tests and Treatment Types

Tests

Treatments

Alternative
Treatments

Mammogram

Radiation Therapy

Bioflavonoid

MRI

Surgery

Mineral
Supplement

X
-
Ray

Hormonal
Therapy

Vitamin
Supplement

Ultrasound

Chemotherapy

Herbal Supplement


Targeted Therapy

Exercise



In order to evaluate performance and quality of medical
data optimization, data quality dimensions used in metrics.
Definitions for quality requirements that are used in the
experimental results section are as follows:



Accuracy: the data should be presented as reality or

verifiable medical resources.



Completeness: all

specific


information


has

to be

represented as co
mplete.



Consistency: data


should


be


represented

without

repetition.



Timeliness: medical data should be stored and

updated

constantly.

Our target is providing the optimal

solution by using these
four quality requirements: the assumption is having the
accuracy, completeness, and consistencies are at a maximum
where as timeliness is at a minimum.

Then, we return the optimization of medical data quality
section of the paper
to illustrate how our proposed approach
should work in this case study. In the optimized data quality
framework in Figure 9, it is shown that the intelligent agent
reports the data quality metrics to the EA and EA finds the
optimal solution for Breast Canc
er Task Sequence. Intelligent
agent duty in the framework is to keep track and verify the
data extraction is completed regarding to the opt
imal task
sequence provided by
the EA.

A.

Experimental Results

This section presents several experimental results to
ind
icate the efficiency of the
multi
-
agent framework based on
web service in medical data quality integrated with the
Evolutionary Algorithm.

Figure 12 shows the efficiency of the algorithm in
maximizing the 3
-
objectives solution space: accuracy,
consistency

and completeness. Each points represent the
combination of service components to complete the web
service task sequence. Our algorithm reaches the fittest task
sequence after 12 generations which is indicated with an
arrow.

Figure 13 and Figure 14 show h
ow Evolutionary Algorithm
can solve multi
-
objective problems by using combination of
different quality metrics with different target points. Such as:

Maximizing accuracy, consistency and completeness, where
as minimizing timeliness.



Fig.12 Fittest Tas
k

Sequence
Representation in terms of Consistency,
Accuracy and Completeness Metrics in 3
-
D Solution Space



Fig
.

13
. Fittest Task Sequence Representation in terms of Timeliness,

Consistency and Completeness Metrics in 3
-
D Solution Space



Fig
.

14
.

Fittest Task Sequence Representation in terms of Timeliness,
Accuracy and Completeness Metrics in 3
-
D Solution Space


Figure 15 shows the efficiency of the EA to optimize the
solution is converged within 12 generations. This convergence
indicates the applicability of the EC algorithms in optimizing
web service task sequence. The algorithm finds the optimized
solution and
also gives alternative solutions for test, treatment
and alternative treatment. The simulation was tested through
set of data quality metrics randomly generated data set by
MATLAB, the results demonstrated that the metric

8

combinations were optimized by the

EC
-
based process.



Fig 15. Fitness Representation of Optimal Service Composition versus
G
enerations


Simulation results of
the algorithm for the top 3

optimized
solutions for Breast Cancer are shown in Table 3.
Evolutionary
Algorithm finds the optimal s
olution and also
gives alternative solutions for tests, treatm
ents and alternative
treatments in the table showing fitness values. According to
the simulation

results
,

the fi
rst optimal solution with highest

fitness is

MRI, Surgery,
Bioflavonoid. Having th
e maximum

fitness values indicates
that
we get better solutio
n, so our goal

is to get the maximum

fitness value which will be closer to 1.


Table 3

Optimized

Fittest

Solutions


Test
s

Treatment
s

Alternative
Treatment
s

Fitness
Value

1

MRI

Surgery

Bioflavonoid

0.
7001

2

MRI

Targeted
Therapy

Bioflavonoid

0.
6066

3

Mammogram

Chemotherapy

Bioflavonoid

0.
5631


VII.

C
ONCLUSION

The accurate judgment about the healthcare treatment
mainly depends on the selection of good quality medical data,
especially in a distributed and heterogeneous healthcare
information environment. This paper proposed a multi
-
agent
framework based on SOA of

Web Service that can assist in
optimizing
m
edical data quality
by
introducing EC
-
process
and implementing

the algorithm

for

the simulation to reach the
optimal solutions by
monitoring the data extraction process,
keeping track of data recoding/retrieval,
and optimizing the
selection of medical data from a distributed and heterogeneous
e
-
healthcare environment. This study starts with creating static
and dynamic models for medical data quality in term of data
extraction so that the domain of objects and proc
esses is
defined. The open design of healthcare intelligent agents
follows the definitions from the medical data quality models.
The design of the intelligent agent enables the intelligent agent
to provide external communication for collaborative service,
internal inference shells for monitoring and tracking of data
extraction process, and printing report service. To solve the
problem of data selection and quality optimization in a
distributed e
-
Healthcare environment, evolution computing
algorithm was inte
grated into the Service Oriented
Architecture of Web Service. In SOA, the healthcare
intelligent agent also plays a major role as the service agent
for medical data registration service and data requesting
service. This multi
-
agent framework has been devel
oped using
Java Expert System Shell (JESS) and Java Agent
DEvelopment Framework (JADE). The system will be
practically deployed and integrated with e
-
Healthcare
information
systems for our local hospitals.

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EFERENCES

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