An Innovative Decision Support
System for diagnosis and treatment
of IBD patients
Evaggelos E. Karvounis*, Vasileios E. Tsianos*, Kallirroi S.
Kyriakidi*, Epameinondas V. Tsianos*,**
*
Research Laboratory of Hepato-Gastroenterology, Division of Gastroenterology, Faculty of Medical School of Health
Sciences, University of Ioannina
** 1st Division of Internal Medicine and Division of Gastroenterology, Faculty of Medical School of Health Sciences,
University of Ioannina
This research project has been co-financed by the European Union (European Regional Development
Fund- ERDF) and Greek national funds through the Operational Program “THESSALY-MAINLAND
GREECE AND EPIRUS - 2007-2013” of the National Strategic Reference Framework (NSRF 20072013)
What is IBD?
• Inflammatory Bowel Disease (IBD) involves chronic inflammation of
all or part of the digestive tract.
• IBD primarily includes ulcerative colitis and Crohn’s disease.
– Ulcerative colitis is a type of inflammatory bowel disease that
causes inflammations in the colon.
• Symptoms include abdominal pain and diarrhea, sometimes bloody.
– Crohn’s disease is another type of IBD that causes
inflammation of the lining of the digestive tract. In CD,
inflammation often spreads deep into affected tissues.
• The inflammation can involve different areas of the digestive tract :
the large intestine, small intestine or both, and/or upper GI tract.
The impact of IBD in society
• The IBD is regarded to represent a multifactorial disease where
genetic factors are interacting with environmental factors.
• The aetiology of IBD still remains unknown.
• The combined use of lifestyle surveys associated with blood
samples and relevant clinical registers seems the best methodology
to identify possible links between genetic predisposition, disease
occurrence and natural course.
All that obviously leads to high financial charges to health services and
implies clear reflection on life quality parameters for the patients and
the society.
Our GOAL
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To extent the data collection process
Digital recording of clinical data
Genetic analysis
Serum analysis of blood samples
Collection of all available data
Efficient analysis using Knowledge Extraction and Data Mining
techniques
• To understand the natural course of the disease, study predisposing
factors and genes and determine early predictors of outcome and
response to treatment techniques.
How can we achieve this goal?
We create an innovative clinical DSS, an efficient web-based platform,
which incorporates 2 modules:
 A Data Repository module: Centralized data repository for
annotation data (clinical, demographic and experimental data),
sample source and handling information, processing and quality
assurance information, as well as inventory and process flow data.
 Knowledge Discovery/Statistics module: Provide tracking, data
query, report generation, process management functions, data
handling as well as statistics, data mining and knowledge extraction
capability. Moreover, the module will contain a Data Representation
module that will handle the presentation of the extracted knowledge
from the patients’ data.
Flow chart system
Ι. DATA COLLECTION / BIOBANK
DECISION SUPPORT SYSTEM
ΙΙ. DATA RECORDING
Environmental Data
Clinical-laboratory tests
Biological Samples
Blood Collection
Collection and storage of
tissue samples
DNA Extraction
Serum Extraction
Gene Study
Serological Study
Recording of the Results
Knowledge Database
ΙΙΙ. DATA ANALYSIS
Export of New Knowledge and Association
Rules
Statistical Analysis
Data Repository module
• A SQL Server database has already been developed
along with the 1st version of the platform.
• Medical data from almost 600 old patients, using their
hard-copy medical records, have been digitized.
• Blood sampling has accomplished in 294 new patients
and 234 healthy volunteers.
• The results of the gene and serological study are
recorded in the database.
Data Repository module
Personal information
Patient's History
Family History
History of the Disease
Medication
Lab Tests
• Name
• Sex
• Date of Birth
• Surgeries
• Infections
• Associated Diseases
• Family History for IBD
• Family History for Other Diseases
• Family History for Cancer
• Established Disease
• Length of CD/UC
• Clinical Assessment
• 5-ASA
• Steroids
• Anti-TNFs
• ASCA
• pANCA
Codify digital data
Insert data in the database (system)
Train the system
Data Repository module
A) Old patients
• Collection of old Patient Medical Records (files) (~ 600)
• Digitization of medical records into electronic files
• Design and implementation of Data Base System (SQL SERVER
2012)
• Copying data to the System Database
Data Repository module
B) New patients
• Collection and reporting of clinical-laboratory data from patients with
IBD (so far has been collected biological material from 294 patients
with IBD and 234 healthy controls).
• Blood sampling after signed informed consent of each patient.
• Bio-samples are processed in the lab (serum, DNA extraction).
• Encoding based on the disease:
◦ Crohn's disease (CD),
◦ Ulcerative colitis (UC),
◦ Indeterminate colitis (IC)
• Storage in -80˚C freezer for further laboratory analysis (gene /
serological study).
Data Repository module
B) New patients
Along with the sample collection, began the initial
experimental procedure with the first laboratory experiments
for the "standardization" of methodology.
Experimental procedure
Our research team is studying variable polymorphisms of
genes and serum biomarkers which, according to the
bibliography, seems to be associated (correlate) with
IBD.
Knowledge Discovery/Statistics module
• The Web application has already been
developed.
• Several knowledge discovery techniques
have been applied in the Databases,
giving more than satisfactory results.
Scenarios
These scenarios present some possible interactions between the user
and the system:
 Shows the probability of a surgery need
 Displays the most appropriate treatment for each individual patient
 Recommends modifications/ changes of the treatment plan for an
individual patient
 Shows probability exacerbations in the disease process
 Shows probability recession disease
 Displays the possible extent of the disease
 Shows the probability of occurrence of extra-intestinal
manifestations
Progress
Phase 1: Defining user requirements, protocol and system design
Phase 2: Development of individual components of the system
Phase 3: Integration and Clinical Evaluation System
Phase 4: Project management and dissemination of results
Phase
1
2
3
4
2013
2014
2015
July AugustSeptember
October
November
December
JanuaryFebruary
MarchApril May June July AugustSeptember
October
November
December
JanuaryFebruary
MarchApril May June July AugustSeptember
October
Future (possible) Cooperation
This system is a decision support system that
aims to be a handy tool for the physician.
In order this system, to provide us the data
we need, it has to be sufficiently (well)
trained.
A large volume of information has to be
inserted.
Need of cooperation!!!
The MORE DATA we get, the SAFER
RESULTS we provide!
Conclusions - Benefits and expansion
options
• The implementation of the proposed project will bring profits not only
to IBD patients (avoiding side effects) but also significant economic
benefits to the health system.
• Furthermore, the development of a predictive model for IBD, can
promote research and proper management of patients in other
health areas except gastroenterology. Safeguarding the rights of
such a program would bring direct benefits in University of Ioannina
as already many organizations and research groups are interested
in developing such a prediction model.
• Through this knowledge, we can easily move to personalized
treatment, bearing in mind there are no diseases, but only patients.
Thank you for your attention!!!
Contact Information
ekarvuni@cc.uoi.gr
vtsianos@cc.uoi.gr
kkyriak@cc.uoi.gr