| QA-driven guidelines generation for bacteriotherapy. | |
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MedLine Citation:
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PMID: 20351908 Owner: NLM Status: In-Process |
Abstract/OtherAbstract:
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PURPOSE: We propose a question-answering (QA) driven generation approach for automatic acquisition of structured rules that can be used in a knowledge authoring tool for antibiotic prescription guidelines management. METHODS: The rule generation is seen as a question-answering problem, where the parameters of the questions are known items of the rule (e.g. an infectious disease, caused by a given bacterium) and answers (e.g. some antibiotics) are obtained by a question-answering engine. RESULTS: When looking for a drug given a pathogen and a disease, top-precision of 0.55 is obtained by the combination of the Boolean engine (PubMed) and the relevance-driven engine (easyIR), which means that for more than half of our evaluation benchmark at least one of the recommended antibiotics was automatically acquired by the rule generation method. CONCLUSION: These results suggest that such an automatic text mining approach could provide a useful tool for guidelines management, by improving knowledge update and discovery. |
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Authors:
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Emilie Pasche; Douglas Teodoro; Julien Gobeill; Patrick Ruch; Christian Lovis |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't Date: 2009-11-14 |
Journal Detail:
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Title: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Volume: 2009 ISSN: 1942-597X ISO Abbreviation: AMIA Annu Symp Proc Publication Date: 2009 |
Date Detail:
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Created Date: 2010-03-30 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101209213 Medline TA: AMIA Annu Symp Proc Country: United States |
Other Details:
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Languages: eng Pagination: 509-13 Citation Subset: IM |
Affiliation:
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Medical Informatics Service, University Hospitals of Geneva and University of Geneva. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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