| Automatically extracting information needs from Ad Hoc clinical questions. | |
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MedLine Citation:
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PMID: 18999100 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Automatically extracting information needs from ad hoc clinical questions is an important step towards medical question answering. In this work, we first explored supervised machine-learning approaches to automatically classify an ad hoc clinical question into general topics. We then evaluated different methods for automatically extracting keywords from an ad hoc clinical question. Our methods were evaluated on the 4,654 clinical questions maintained by the National Library of Medicine. Our best systems or methods showed F-score of 76% for the task of question-topic classification and an average F-score of 56% for extracting keywords from ad hoc clinical questions. |
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Authors:
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Hong Yu; Yong-Gang Cao |
Publication Detail:
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Type: Journal Article; Research Support, U.S. Gov't, P.H.S. Date: 2008-11-06 |
Journal Detail:
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Title: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Volume: - ISSN: 1942-597X ISO Abbreviation: - Publication Date: 2008 |
Date Detail:
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Created Date: 2008-11-12 Completed Date: 2010-01-08 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: 96-100 Citation Subset: IM |
Affiliation:
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Departments of Health Sciences, Computer Science, Medical InformatiUniversity of Wisconsin-Milwaukee, Wisconsin, USA. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Artificial Intelligence* Communication Decision Support Systems, Clinical* Information Dissemination / methods* Internet* Natural Language Processing* Pattern Recognition, Automated / methods Point-of-Care Systems Remote Consultation / methods* User-Computer Interface* |
| Grant Support | |
ID/Acronym/Agency:
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1R01LM009836-01A1/LM/NLM NIH HHS |
| Comments/Corrections | |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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