MITRE system for clinical assertion status classification.
Objective: To describe a system for determining the assertion status of medical problems mentioned in clinical reports, which was entered in the 2010 i2b2/VA community evaluation 'Challenges in natural language processing for clinical data' for the task of classifying assertions associated with prob...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 18; no. 5; pp. 563 - 568 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104577235&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104577235 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Sep2011 vid: 18 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104577235 104577235 NLM21515542 2011241434 10.1136/amiajnl-2011-000164 NLM21515542 104577235 ppf: 563 ppct: 5 formats: tig: atl: MITRE system for clinical assertion status classification. aug: au: Clark C Aberdeen J Coarr M Tresner-Kirsch D Wellner B Yeh A Hirschman L Clark, Cheryl Aberdeen, John Coarr, Matt Tresner-Kirsch, David Wellner, Ben Yeh, Alexander Hirschman, Lynette affil: The MITRE Corporation, Bedford, Massachusetts 01730-1420, USA sug: subj: Data Mining Classification Decision Support Systems, Clinical Classification Electronic Health Records Classification Natural Language Processing Information Science Classification Cues Semantics Uncertainty ab: Objective: To describe a system for determining the assertion status of medical problems mentioned in clinical reports, which was entered in the 2010 i2b2/VA community evaluation 'Challenges in natural language processing for clinical data' for the task of classifying assertions associated with problem concepts extracted from patient records.Materials and Methods: A combination of machine learning (conditional random field and maximum entropy) and rule-based (pattern matching) techniques was used to detect negation, speculation, and hypothetical and conditional information, as well as information associated with persons other than the patient.Results: The best submission obtained an overall micro-averaged F-score of 0.9343.Conclusions: Using semantic attributes of concepts and information about document structure as features for statistical classification of assertions is a good way to leverage rule-based and statistical techniques. In this task, the choice of features may be more important than the choice of classifier algorithm. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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