A flexible framework for deriving assertions from electronic medical records.
Objective: This paper describes natural-language-processing techniques for two tasks: identification of medical concepts in clinical text, and classification of assertions, which indicate the existence, absence, or uncertainty of a medical problem. Because so many resources are available for process...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 18; no. 5; pp. 568 - 574 |
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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=104577257&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104577257 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: 104577257 104577257 NLM21724741 2011241456 10.1136/amiajnl-2011-000152 NLM21724741 104577257 ppf: 568 ppct: 6 formats: tig: atl: A flexible framework for deriving assertions from electronic medical records. aug: au: Roberts K Harabagiu SM Roberts, Kirk Harabagiu, Sanda M affil: Human Language Technology Research Institute, University of Texas at Dallas, Richardson, Texas 75080-0688, USA sug: subj: Data Mining Classification Decision Support Systems, Clinical Classification Electronic Health Records Classification Natural Language Processing Algorithms Semantics Uncertainty ab: Objective: This paper describes natural-language-processing techniques for two tasks: identification of medical concepts in clinical text, and classification of assertions, which indicate the existence, absence, or uncertainty of a medical problem. Because so many resources are available for processing clinical texts, there is interest in developing a framework in which features derived from these resources can be optimally selected for the two tasks of interest.Materials and Methods: The authors used two machine-learning (ML) classifiers: support vector machines (SVMs) and conditional random fields (CRFs). Because SVMs and CRFs can operate on a large set of features extracted from both clinical texts and external resources, the authors address the following research question: Which features need to be selected for obtaining optimal results? To this end, the authors devise feature-selection techniques which greatly reduce the amount of manual experimentation and improve performance.Results: The authors evaluated their approaches on the 2010 i2b2/VA challenge data. Concept extraction achieves 79.59 micro F-measure. Assertion classification achieves 93.94 micro F-measure.Discussion: Approaching medical concept extraction and assertion classification through ML-based techniques has the advantage of easily adapting to new data sets and new medical informatics tasks. However, ML-based techniques perform best when optimal features are selected. By devising promising feature-selection techniques, the authors obtain results that outperform the current state of the art.Conclusion: This paper presents two ML-based approaches for processing language in the clinical texts evaluated in the 2010 i2b2/VA challenge. By using novel feature-selection methods, the techniques presented in this paper are unique among the i2b2 participants. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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