| Enhancing clinical problem lists through data mining and natural language processing. | |
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MedLine Citation:
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PMID: 18999076 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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The availability of timely, accurate, comprehensive, and coded clinical problem lists is essential for supporting a range of healthcare activities. Evidence and experience suggest, however, that problem lists are frequently out-of-date, sometimes omit clinically important problems, and contain uncoded entries. Here, we describe a study being performed at Partners HealthCare System to explore automated techniques for enhancing existing problem lists. |
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Authors:
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Elizabeth S Chen; Adam Wright; Francine L Maloney; Cheryl Van Putten; Marilyn D Paterno; Howard S Goldberg |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't 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: 901 Citation Subset: IM |
Affiliation:
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Clinical Informatics Research & Development, Partners HealthCare System, Wellesley, MA, USA. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Artificial Intelligence Information Storage and Retrieval / methods Medical History Taking / methods* Medical Records Systems, Computerized / organization & administration* Medical Records, Problem-Oriented* Natural Language Processing* Pattern Recognition, Automated / methods* Quality Assurance, Health Care / methods* United States |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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