Using literature mining to 
explore concept complexity in 
obesity
George Karystianis
School of Computer Science 
Supervis...
2
Obesity (1)
●
  Complex/underlying epidemic
●
 Worldwide problem
●
 Related to various diseases
●
 Various aspects
Obesity (2)
Obesity concept map
5
Motivation and Aim
●
Assist clinicians/researchers in representation                 
  and validation of their knowledg...
6
Objectives
●
To generate a set of methods to detect obesity related 
concepts in literature.
●
To validate obesity infor...
7
Text mining 
●
 Extraction of information from unstructured data.
●
 Performed on documents with complex and specific   ...
8
Concept Maps
●
 Knowledge representation model.
●
 Constructed by concepts and links.
●
 Gather, understand, explore kno...
9
Overview of the project
Medical
literature
Epidemiological
data
Text mining
techniques
Concept
map
Validation
Enhancemen...
What are we looking for?
– Risk factors
– Causal factors
– Confounding factors
– Complications
– Interventions
– Outcomes ...
11
Example
12
Methodology overview (1)
PubMed
Obesity
Literature
Analysis
Semantic
Analysis
Triggers
Set of rules
Information
Extract...
13
Methodology overview (2)
Information
Extraction
Engine
Term
recognition
Term
structuring
Pattern
matching
Important
ter...
14
Results
15
Evaluation
●
 Compare the results from the use of text mining             
  methods with the concept map ones.
●
 Are ...
16
Summary
Use Text Mining methods to:
●
 Extract risk, causal factors, complications, etc.
●
 obtain a better understandi...
17
Thank you for attending and  for listening.
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Phd presentation in health informatics

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Phd presentation in health informatics

  1. 1. Using literature mining to  explore concept complexity in  obesity George Karystianis School of Computer Science  Supervisors Goran Nenadic, Iain Buchan
  2. 2. 2 Obesity (1) ●   Complex/underlying epidemic ●  Worldwide problem ●  Related to various diseases ●  Various aspects
  3. 3. Obesity (2)
  4. 4. Obesity concept map
  5. 5. 5 Motivation and Aim ● Assist clinicians/researchers in representation                    and validation of their knowledge. ­ Assist in health care improvement. ●  Exploration of medical knowledge. ●  Enhance the understanding of the health concepts in         obesity. ●  Design a framework for generation (or improvement of      existing) of medical concept maps.
  6. 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. 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. 8 Concept Maps ●  Knowledge representation model. ●  Constructed by concepts and links. ●  Gather, understand, explore knowledge. ●  Variety of users. ●  No explicit detail. ●  Implementations mainly in education.
  9. 9. 9 Overview of the project Medical literature Epidemiological data Text mining techniques Concept map Validation Enhancement Results Improved Concept map
  10. 10. What are we looking for? – Risk factors – Causal factors – Confounding factors – Complications – Interventions – Outcomes (primary, secondary) – Exposures
  11. 11. 11 Example
  12. 12. 12 Methodology overview (1) PubMed Obesity Literature Analysis Semantic Analysis Triggers Set of rules Information Extraction Engine Results Modelling
  13. 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. 14 Results
  15. 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. 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. 17 Thank you for attending and  for listening.

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