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QA-driven guidelines generation for bacteriotherapy

Published in AMIA Annual Symposium. Proceedings. 2009, vol. 2009, p. 509-13
Abstract 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.
Keywords Anti-Bacterial Agents/therapeutic useAutomatic Data ProcessingBacterial Infections/drug therapyData MiningDecision Support TechniquesHumansInformation Storage and RetrievalPractice Guidelines as TopicPubMedSearch EngineVocabulary, Controlled
PMID: 20351908
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Research group Interfaces Homme-machine en milieu clinique (610)
Project FP7: DebugIT
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PASCHE, Emilie et al. QA-driven guidelines generation for bacteriotherapy. In: AMIA Annual Symposium proceedings, 2009, vol. 2009, p. 509-13. https://archive-ouverte.unige.ch/unige:28898

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Deposited on : 2013-07-12

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