Phd presentation in health informatics

Using literature mining to explore concept complexity in 

obesity

George KarystianisSchool of Computer Science 

SupervisorsGoran Nenadic, Iain Buchan

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Obesity (1)

●  Complex/underlying epidemic

● Worldwide problem

● Related to various diseases

● Various aspects

Obesity (2)

Obesity concept map

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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.

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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.

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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.

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Concept Maps

● Knowledge representation model.

● Constructed by concepts and links.

● Gather, understand, explore knowledge.

● Variety of users.

● No explicit detail.

● Implementations mainly in education.

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Overview of the project

Medicalliterature

Epidemiologicaldata

Text miningtechniques

Concept map

Validation

Enhancement

Results

ImprovedConcept map

What are we looking for?

– Risk factors– Causal factors– Confounding factors– Complications– Interventions– Outcomes (primary, secondary)– Exposures

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Example

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Methodology overview (1)

PubMedObesity

LiteratureAnalysis

SemanticAnalysis

Triggers

Set of rules

Information Extraction

Engine

Results

Modelling

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Methodology overview (2)Information Extraction

Engine

Term recognition

Termstructuring

Pattern matching

Important terms

Patterns

Terminology heads

Termclass

Terminology identification Pattern recongition

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Results

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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.

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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. 

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Thank you for attending and  for listening.