EXPERIMENTAL DATABASE IMPLEMENTATION FOR THE

EREBROVASCULAR DISEASES RESEARCH INTEGRATES

TOGETHER DIFFERENT KINDS OF MEDICAL DATA

Petr Vˇcel´ak, Jana Kleˇckov´a

Department of Computer Science and Engineering, University of West Bohemia, Univerzitni 8, Pilsen, Czech Republic

Vladim´ır Rohan

Department of Neurology, University Hospital, Pilsen, Czech Republic

Keywords:

Cerebrovascular diseases, Clinical data, DASTA, Database, DICOM, Medical data, Metadata, Resource De-

scription Framework (RDF), Stroke, Thrombolysis.

Abstract:

The cerebrovascular diseases are one the most common cause of death worldwide. The second most frequent

cause of death in the Czech Republic. The proposed database should notably contribute to the solution of this

complex problem. Its proﬁt is based on medical data interconnection and aggregation of collaborating centers.

There are stored miscellaneous de-indentiﬁed medical data in the database such as (1) a set of patient’s clinical

data in a DASTA ﬁle format, (2) a set of brain scans like computed tomography in a DICOM ﬁles, (3) data

from Safe Implementation of Thrombolysis in Stroke (SITS) register. The experimental database project has

an extensible support for any other ﬁtted data format.

1 INTRODUCTION

Stroke is one of the leading causes of morbidity and

mortality worldwide (Lopez and Blobel, 2009). Large

differences in incidence, prevalence and mortality

have been noted between Eastern and Western Eu-

rope. This has been attributed to differences in risk

factors, with higher levels of hypertension and other

risk factors resulting in more severe stroke in East-

ern Europe (Brainin et al., 2000). Notable regional

variations have also been found within Western Eu-

rope. Stroke is the most important cause of mor-

bidity and long term disability in Europe, and demo-

graphic changes will result in an increase in both inci-

dence and prevalence. It is also the second most com-

mon cause of dementia, the most frequent cause of

epilepsy in the elderly, and a frequent cause of depres-

sion. (Awareness et al., 2008; Rothwell et al., 2005;

O’Brien et al., 2003) The cerebrovasculardiseases are

one the most common cause of death worldwide. It is

the second most frequent cause of death in the Czech

Republic. Cancer incidence and mortality are two to

three times greater in the Czech Republic than in other

developed countries in Europe. (MZ

CR – Ministry of

Health of the Czech Republic, 2010;

U – Czech

Statistical Ofﬁce, 2010)

The experimental database is as a meta data min-

ing tool used primarily in a research. It allows study-

ing of crucial values and dependencies of particular

parameters in high amount of examinations with clin-

ical data correlations. Captured data and obtained

knowledge should notably contribute to the solution

of complex cerebrovascular brain diseases problem.

Later, the database can be used as a practical educa-

tion and diagnostic tool. The experimental database

concept was announced in (Vˇcel´ak et al., 2009).

2 MEDICAL DATA TYPE

SUPPORT

This paper experiments and hypothesis are based on

an evaluation of heterogeneous medical data. It is a

patient’s clinical data set in a DASTA (Karlova uni-

verzita v Praze – 2. l´ekaˇrsk´a fakulta v Praze (Charles

University in Prague – 2nd Faculty of Medicine),

2010) format and radiological studies in a DICOM

format (National Institute of Neurological Disorders

and Stroke, 2010) of patients affected by a stroke at

111

celák P., Kle

cková J. and Rohan V. (2010).

EXPERIMENTAL DATABASE IMPLEMENTATION FOR THE CEREBROVASCULAR DISEASES RESEARCH INTEGRATES TOGETHER DIFFERENT

KINDS OF MEDICAL DATA.

In Proceedings of the Multi-Conference on Innovative Developments in ICT, pages 111-114

DOI: 10.5220/0003037501110114

 SciTePress

the University hospital in Pilsen. The hospital is a

participant of the Safe Implementation of Thrombol-

ysis in Stroke (SITS) program, an internet-based in-

teractive thrombolysis register (Safe Implementation

of Thrombolysis in Stroke, 2010). Medical doctor

records details about a stroke into the SITS register.

The SITS register contains e.g. therapeutic details,

adverse drug reaction, National Institutes of Health

(NIH) data, computed tomography (CT) description,

death cause description. These data are not structured

in a clinical data from the hospital information sys-

tem. Feedback missing in the SITS register is a huge

disadvantage for a medical doctor. That is why we

are trying to interconnects clinical and imaging data

(Rohan et al., 2007) with SITS the register data in the

experimental database.

Patient’s health records are provided mostly in the

native language text data. A laboratory report is a

structured document used while making a decision,

calculation or graphing. These clinical data can be

stored in a DASTA or a HL7 format. A diagnos-

tic imaging procedures creates multimedia data, that

originates from e.g. electroencephalograph (EEG)

and Computed Tomography (CT).

2.1 DASTA Type

The DASTA is Data Standard abbreviation (Karlova

univerzita v Praze – 2. l´ekaˇrsk´a fakulta v Praze

(Charles University in Prague – 2nd Faculty of

Medicine), 2010). It is a national electronic commu-

nication standard format of a public health service in

the Czech Republic. The DASTA is a Health Level 7

(Health Level Seven, Inc., 2010) standard equivalent

and was developed by the Czech Public Health Infor-

matics and Scientiﬁc Information Organization sup-

ported by the Minister of Health of the Czech Repub-

lic. It is based on the XML-based mark-up standard

with XSD schema.

2.2 Health Level Seven Type

Health Level Seven (HL7) is a non-proﬁt organization

involved in the development of international health-

care standards. It is widely used for interchange be-

tween hospitals and physician record systems and be-

tween electronic medical record systems and prac-

tice management systems. (Health Level Seven, Inc.,

2010)

The HL7 Clinical Document Architecture (CDA)

documents are used to communicate documents such

as physician notes and other material (Dolin et al.,

2006). The CDA is an XML-based mark-up stan-

dard intended to specify the encoding, structure and

semantics of clinical documents. The CDA docu-

ment consists of a mandatory textual part and optional

structured parts. The mandatory textual part ensure

human interpretation of the document contents.

The experimental database bargain for the HL7

documents support. The HL7 storage is supported,

but a next layer with HL7 format support is not im-

plemented, at this time.

2.3 DICOM Type

The Digital Imaging and Communications in

Medicine (DICOM) standard has been developed

with an emphasis on diagnostic medical imaging. It

is applicable to a wide range of image and non-image

related information exchanged in clinical and other

medical environments. This standard is widely

used for representing and communicating radiology

images scans and reporting. The DICOM standard is

supported by the National Electrical Manufacturers

Association. (National Institute of Neurological

Disorders and Stroke, 2010)

The searching in the set of DICOM ﬁles has some

difﬁculties. You need a special library to acquire any

information from the DICOM ﬁle.

2.4 The SITS Register Data

The SITS register is an academic driven, non-proﬁt,

international collaboration. It is an initiative by the

medical professionals to certify excellence in acute

stroke treatment. The SITS initiated an internet-based

interactive thrombolysis register, to serve as an instru-

ment for clinical centers to follow their own treatment

results and compare with other centers in their coun-

tries and in the collaborating countries. (Safe Imple-

mentation of Thrombolysis in Stroke, 2010)

Treatment results are not available to a medical

doctor for reuse this data in a structured way for a re-

search purpose. We created a set of scripts that down-

load and parse a treatment ﬁle report page.

3 DATA PROCESSING

The research is based on an anonymous data only. All

of identiﬁcation information are necessarily removed

before an upload from the hospital to the experimen-

tal database. The data anonymization process kept

all relationships. That is why we can use so much

more data to research. The patient’s clinical data are

stored together with imaging examinations in a single

database. It can be extended with any other proper

kind of data.

INNOV 2010 - International Multi-Conference on Innovative Developments in ICT

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The hospital information system exports a particu-

lar patient with a stroke medical data sets into a single

directory. There are exported DASTA and DICOM

ﬁles with unique names. DASTA ﬁle links all clinical

event relevant DICOM ﬁles. The links are DICOM

ﬁle names. The DASTA ﬁle can contain the SITS reg-

ister identiﬁcator in a comment attribute. Each stroke

corresponds to one SITS register identiﬁcator. De-

identiﬁed DASTA, DICOM and created SITS-XML

ﬁles are raw input ﬁles in the experimental system.

All raw input ﬁles are processed and extraction of

meta data is done. A new information or knowledge

is referred as a meta data. We decided to use a modern

Resource Description Framework (RDF) model for a

meta data manipulation (W3C, 2010). The raw data

processing tool is extensible. It can be a simple script

that extracts any attribute value or an application with

complex queries, images evaluation, computing, etc.

A processing tool can operate on a meta data extracted

previously, not only on a raw data.

The advantage of our experimental database is the

incremental growing of a RDF model and increasing

knowledgebase. Suppose you are interested in a treat-

ment time delay of a stroke. First, you know nothing,

except the algorithm. So you implement an evalua-

tion of a time interval between a stroke and a treat-

ment’s begin. The RDF model will be extended after

the implemented algorithm execution. After that, you

are able to conﬁrm a treatment time delay impression

on patients treatment result. Other advantage exam-

ple is when you want to search for a set of speciﬁc

ﬁles based on any conditions then it will be too time

consuming to process the raw data. Apparently it is

a better way to acquire useful data earlier and store

them into the RDF graph. Then the search can be

more quickly and efﬁciently.

4 HYPOTHESIS AND

EXPERIMENTS

4.1 Data Set Description

The whole stroke data set characteristic in the experi-

mental database is:

• Stroke was in years 2005 - 2009

• Patient count 244; Middle age 68

• Male/Female count 148 / 96

• Male/Female middle age 66.5 / 71

• Male/Female average age 66.1 / 67.2

4.2 Patient’s Age and Sex Distribution

Statistic

Advancedage is one of the most signiﬁcantstroke risk

factors. The incidence of stroke increases exponen-

tially from 30 years of age, and etiology varies by age

(Ellekjær et al., 1997). Our real patient’s data set are

shown on the Figure 1 grouped by age and sex. It

shows us that 95 % of strokes mostly occur in people

age 45 and older. Two-thirds of strokes occur in those

over the age of 65. Similar results are also referred in

(Senelick and Dougherty, 2001; National Electrical

Manufacturers Association (NEMA), 2010). A per-

son’s risk of dying if he or she does have a stroke in-

creases with age. However, it is evidence that stroke

can occur at any age.

Figure 1: Patient’s Age and Sex distribution over the stroke

data set.

4.3 rt-PA Treatment Time Delay

The last basic example of data usage is a treatment

time delay evaluation. It is a time interval between

a stroke and an rt-PA treatment begin. The distribu-

tion of the treatment time delay by hours is shown

on a Figure 2. Any more complex query evaluation

research can continue with this values, e.g. we can

identify dependency between the treatment time de-

lays and a treatment success.

Figure 2: Treatment time delay. It is a time interval between

a patient’s stroke and begin of an rt-PA treatment.

EXPERIMENTAL DATABASE IMPLEMENTATION FOR THE CEREBROVASCULAR DISEASES RESEARCH

INTEGRATES TOGETHER DIFFERENT KINDS OF MEDICAL DATA

113

5 CONCLUSIONS

The collaboration vision of medical doctor and com-

puter programmer team were established. The medi-

cal doctor informs what to do with the data and how

to interpret data for the research purposes. The com-

puter programmer creates and implements a right al-

gorithm. The experimental database is ready to new

participants, now.

The project beneﬁts from the medical data rela-

tionships. Related data and a RDF data model is the

project base. Meta data stored in a RDF format are ac-

quired by raw and previously processed medical data

processing. By this way we create information and

build a knowledge base. The knowledge base speed

up and ease searching for resources. A user can take

advantage of this meta data referred to the raw medi-

cal data. It is better to querying in a huge amount of

data. Generally, the meta data can contain any data

type. With RDF model we can refer to any data type.

Basic part of the project is implemented and all

ordinary medical data types are supported. Currently,

we are working on a new data mining algorithms that

extends the project knowledge base.

ACKNOWLEDGEMENTS

The work presented in this paper was supported

by the project Czech Science Foundation number

106/09/0740.

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