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Medical Information Retrieval: eEvidence System

Medical Information Retrieval: eEvidence System. By Zhao Jin Mar-12-2010. 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

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Medical Information Retrieval: eEvidence System

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  1. Medical Information Retrieval: eEvidence System By Zhao JinMar-12-2010

  2. 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

  3. Outline • What is Evidence-based Practice (EBP) • How EBP is implemented and what are the issues • Design of eEvidencesystem • Discussion and Future work

  4. 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

  5. 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

  6. 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

  7. 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

  8. eEvidence-based System Medical Websites Crawler Classifier/Extractor Webpages Classification / Extracted Data Indexer Index Read Interface Search Interface Profile Users

  9. Crawling • Implemented with Nutch • Periodical crawling on websites selected by experts • Advantage: • Generic, available, updated, flexible

  10. Classification and Extraction • Type classificationon webpages • Three classes: Abstract, full text and others • Ensure proper organization of search results and filter out unusefulwebpages • Key sentence and word extraction • Maxent classification with text features, parse features and medical features

  11. Dual Interface (Read)

  12. Dual Interface (Search)

  13. 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

  14. Some results… Type Classification Sentence Extraction Word Extraction

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