Medical Information Retrieval: eEvidence System By Zhao Jin Mar

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

Outline What is Evidence-based Practice (EBP) How EBP is implemented and what are the issues Design of eEvidence system Discussion and Future work

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

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

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

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

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

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

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

Dual Interface (Read)

Dual Interface (Search)

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

Some results… PrecisionRecallF 1 -Measure Patient Result Intervention Study Design Research Goal PrecisionRecallF 1 -Measure Abstract Full text Others PrecisionRecallF 1 -Measure Age Gender Condition Race --- Intervention Study Design Type Classification Sentence Extraction Word Extraction