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Slides for Knowledge Extraction Presentation
Transcript of Slides for Knowledge Extraction Presentation
Challenge Levels of Medical Image Inspection
• Perceptual inspection – involves visual feature saliency, experts’ inspecting
strategies, their habits, etc.
• Conceptual reasoning – requires domain knowledge and clinical
experience, etc.
• Challenge Levels of these images are what physicians perceive and can be measured by their performances.
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Why is it important to study challenge levels of medical image inspections?
• Help organize medical image-cases based on their challenge levels to physicians
– Closer to physicians’ mental model.
• Help devise medical education system as another application
– Medical images of different challenge levels--> different levels of expertise
– Provide tasks based on performance
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Computational Linguistic
• Computational manipulation of natural language -> we call it “NLP”
• Usually use “Natural Language Toolkit (NLTK)”
– Semantic hierarchical structure -> we use “WordNet”, distance & similarity
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WordNet: An Example
• Fragment of WordNet Concept Hierarchy: nodes correspond to synsets; edges indicate the hypernym/hyponym relation, i.e. the relation between superordinate and subordinate concepts.
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Computational Linguistic
WordNet Lack of medical concepts
Therefore, we use UMLS
– Medical knowledge-base (concepts and relations)
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My Work
• Linguistic Data Preprocessing – (UMLS) MetaMap for mapping medical texts (in natural
language) into medical concepts
– Used for detecting medical concepts (thus filtering out function words)
• Measuring “Challenge Levels” by defining: – Lexical Consistency (among physicians)
– Conceptual Relatedness (to correct diagnosis)
• Clustering results – Verify the usefulness of (1) verbal narratives, and of (2)
my proposed metrics (consistency and relatedness). 10
Linguistic Data Preprocessing
• A use of domain ontology (UMLS)
• UMLS helps analyze lower level raw data collected from verbal descriptions
• Extracting domain knowledge that is conveyed by medical terms
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Lexical Consistency
• Physicians are same/similar in use of medical concepts?
Subj. erythematous cheek … psoriasis
1 × × … ×
2 × − … ×
… … … … …
16 × × … −
Subj. erythematous cheek … psoriasis
1 × − … −
2 × − … −
… … … … …
16 × − … −
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3[ 1, 1, 1 ∙ 1, 0, 1 /6 +
1, 1, 1 ∙ 1, 1, 0 /6 +
1, 0, 1 ∙ 1, 1, 0 /4] =𝟏𝟏
𝟑𝟔
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3[ 1, 0, 0 ∙ 1, 0, 0 /1 +
1, 0, 0 ∙ 1, 0, 0 /1 +
1, 0, 0 ∙ 1, 0, 0 /1] =𝟑𝟔
𝟑𝟔
cosine similarity = 𝐴·𝐵
𝐴 𝐵
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Lexical Consistency – A Difficult Case
Impetigo Atopic dermatitis Zinc deficiency syndrome Kawasaki's Candida Infected with strep or staph Slap cheek syndrome … Acrodermatitis enteropathica
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Lexical Consistency - Rationale
• Physicians we recruited, with high level of expertise, cannot make the same misdiagnosis.
– For an easy image case, physicians could arrive at the correct diagnosis by observing different clues.
– For a difficult image case, physicians may arrive at different incorrect diagnosis by noticing different tricky clues.
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Conceptual Relatedness
• Path-based Algorithms
• Definition-based Algorithms
– Definition of each disease → compare vector similarity
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Conceptual Relatedness: An Example
highly-related diagnoses
incorrect diagnoses
somewhat related descriptions
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Conceptual Relatedness - Rationale
• More relevant speech about the correct diagnosis, more knowledge on this topic.
– To judge the expertise of physicians in training.
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