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Phd presentation in health informatics

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Phd presentation in health informatics Presentation Transcript

  • 1. Using literature mining to  explore concept complexity in  obesity George Karystianis School of Computer Science  Supervisors Goran Nenadic, Iain Buchan
  • 2. Obesity (1)   Complex/underlying epidemic ● ●  Worldwide problem ●  Related to various diseases ●  Various aspects 2
  • 3. Obesity (2)
  • 4. Obesity concept map
  • 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. 5
  • 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. 6
  • 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. ● 7
  • 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. 8
  • 9. Overview of the project Epidemiological Concept data map Validation Text mining Results techniques Enhancement Medical Improved literature Concept map 9
  • 10. What are we looking for? – Risk factors – Causal factors – Confounding factors – Complications – Interventions – Outcomes (primary, secondary) – Exposures
  • 11. Example 11
  • 12. Methodology overview (1) Obesity PubMed Analysis Literature Semantic Triggers Analysis Information Extraction Set of rules Engine Results Modelling 12
  • 13. Methodology overview (2) Information Extraction Engine Terminology identification Pattern recongition Term Term Terminology Pattern recognition structuring heads matching Important Term Patterns terms class 13
  • 14. Results 14
  • 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. 15
  • 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.  16
  • 17. Thank you for attending and  for listening. 17