1. Using literature mining to
explore concept complexity in
obesity
George Karystianis
School of Computer Science
Supervisors
Goran Nenadic, Iain Buchan

2. 2
Obesity (1)
●
Complex/underlying epidemic
●
Worldwide problem
●
Related to various diseases
●
Various aspects

3. Obesity (2)

4. Obesity concept map

5. 5
Motivation and Aim
●
Assist clinicians/researchers in representation
and validation of their knowledge.
­ Assist in health care improvement.
●
Exploration of medical knowledge.
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Enhance the understanding of the health concepts in
obesity.
●
Design a framework for generation (or improvement of
existing) of medical concept maps.

6. 6
Objectives
●
To generate a set of methods to detect obesity related
concepts in literature.
●
To validate obesity information.
– discover any significant differences in the
understanding of the disease.
●
To integrate data and literature.
– discover new knowledge related to obesity.
●
To provide and evaluate a framework for
building and validation of medical concept maps.

7. 7
Text mining
●
Extraction of information from unstructured data.
●
Performed on documents with complex and specific
terminology and expressions.
●
Challenges:
­Ambiguity, Synonyms, fuzzy conclusions.
●
Various tools and applications available.
●
Adaptation to user's and task needs.

8. 8
Concept Maps
●
Knowledge representation model.
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Constructed by concepts and links.
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Gather, understand, explore knowledge.
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Variety of users.
●
No explicit detail.
●
Implementations mainly in education.

9. 9
Overview of the project
Medical
literature
Epidemiological
data
Text mining
techniques
Concept
map
Validation
Enhancement
Results
Improved
Concept map

10. What are we looking for?
– Risk factors
– Causal factors
– Confounding factors
– Complications
– Interventions
– Outcomes (primary, secondary)
– Exposures

11. 11
Example

12. 12
Methodology overview (1)
PubMed
Obesity
Literature
Analysis
Semantic
Analysis
Triggers
Set of rules
Information
Extraction
Engine
Results
Modelling

13. 13
Methodology overview (2)
Information
Extraction
Engine
Term
recognition
Term
structuring
Pattern
matching
Important
terms
Patterns
Terminology
heads
Term
class
Terminology identification Pattern recongition

14. 14
Results

15. 15
Evaluation
●
Compare the results from the use of text mining
methods with the concept map ones.
●
Are these terms:
a) important?
b) related to obesity?
c) common?
●
Examination and classification of the new concepts/
links through experts.
­Validation/enhancement of the concept map.

16. 16
Summary
Use Text Mining methods to:
●
Extract risk, causal factors, complications, etc.
●
obtain a better understanding of obesity concepts.
●
provide a framework for building of medical concept
maps.

17. 17
Thank you for attending and for listening.