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Medical Information Retrieval: eEvidence System
By Zhao JinMar-12-2010
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Domain-specific Information Retrieval
• Research– What are the characteristics of the users, the documents
and the search process in a specific domain? – What changes should be made in a IR system?
• Domains– Math
• User study, Prototype Implementation, Probabilistic Framework and Iterative Readability Computation
– Medical• eEvidence system for evidence-based practice
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Outline
• What is Evidence-based Practice (EBP)• How EBP is implemented and what are the
issues• Design of eEvidence system• Discussion and Future work
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Evidence-based practice (EBP)
• Decide what to do with the patients based on research findings– Instead of common sense, conventions, etc.
• Promote the publication and use of reviews and summaries of research articles
• Advantage:– Satisfy the information needs of the practitioners– Reduce the amount of literature to keep up with– Accelerate the implementation of research findings
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Implementation of EBP• Guideline (active search)– Form clinical question– Identify key elements
• Patient, Intervention, Comparison, Outcome
– Search EBP resources• Availability• Applicability• Validity / Strength of evidence (Study Design)
• Issues– Generic vs Specialized search engine– Hard to assess applicability and validity– Time constraint
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Implementation of EBP
• Alternative (passive search)– Receive suggestion/support while working • Knowledge-based system• Decision support system (meta-search)
• Issues– Less precise– Limited resources– Difficult to encode and update findings
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eEvidence System
• Features– Crawling-based• Generic, available, updated and flexible
– Automatic Classification and Extraction• More organized results• Applicability and Validity assessment
– Dual Interface• Different seeking behaviors
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eEvidence-based System
Medical Websites
WebpagesClassification / Extracted Data
Index
Read Interface
Search Interface
Profile
Users
CrawlerClassifier/Extractor
Indexer
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Crawling• Implemented with Nutch
• Periodical crawling on websites selected by experts
• Advantage:– Generic, available, updated, flexible
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Classification and Extraction
• Type classification on webpages– Three classes: Abstract, full text and others– Ensure proper organization of search results and filter out
unuseful webpages
• Key sentence and word extraction
• Maxent classification with text features, parse features and medical features
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Dual Interface (Read)
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Dual Interface (Search)
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Discussion & Future work
• Size of article collection– 17 websites, 16,522 abstracts and 3371 full text articles– Not large enough for evaluation with practical task
• Classification and extraction– Good accuracy on webpage type classification, to be
extended to more types– High precision but low recall on sentence extraction– Handling of word classes with open-vocabulary still
tricky
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Some results…
Precision Recall F1-MeasurePatient .68 .21 .33 Result .81 .55 .66 Intervention .84 .22 .35 Study Design .94 .30 .45 Research Goal .93 .37 .53
Precision Recall F1-MeasureAbstract .95 .98 .97Full text .94 .97 .96Others .99 .98 .99
Precision Recall F1-MeasureAge .74 .52 .61 Gender .89 .68 .77Condition .58 .49 .53 Race - - - Intervention .59 .45 .51Study Design .84 .73 .78
Type Classification
Sentence Extraction
Word Extraction