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Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
Phd presentation in health informatics
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Phd presentation in health informatics

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  • 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. ●  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. ●  Constructed by concepts and links. ●  Gather, understand, explore knowledge. ●  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.

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