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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.