Natural Language Processing Technologies in Radiology Research and Clinical Applications.
The migration of imaging reports to electronic medical record systems holds great potential in terms of advancing radiology research and practice by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due...
| Publicado en: | RadioGraphics Vol. 36; no. 1; pp. 176 - 192 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
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Wolters Kluwer Health
2016 Jan-Feb
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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=112296431&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 112296431 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02715333 4ZQC jtl: RadioGraphics issn: 02715333 maglogo: N pubinfo: dt: 2016 Jan-Feb vid: 36 iid: 1 pid: 79357 pub: Wolters Kluwer Health place: New York, New York artinfo: ui: 112296431 112296431 NLM26761536 112296431 10.1148/rg.2016150080 NLM26761536 PMC4734053 [Available on 01/01/17] 112296431 ppf: 176 ppct: 16 formats: tig: atl: Natural Language Processing Technologies in Radiology Research and Clinical Applications. aug: au: Cai, Tianrun Giannopoulos, Andreas A Yu, Sheng Kelil, Tatiana Ripley, Beth Kumamaru, Kanako K Rybicki, Frank J Mitsouras, Dimitrios affil: From the Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115 (T.C., A.A.G., K.K.K., F.J.R., D.M.); Harvard T.H. Chan School of Public Health, Boston, Mass (S.Y.); and Department of Radiology, Brigham and Women's Hospital, Boston, Mass (T.K., B.R.) sug: subj: Vocabulary, Controlled Natural Language Processing Research, Medical Specialties, Medical Data Mining Methods Information Science Methods Human Funding Source ab: The migration of imaging reports to electronic medical record systems holds great potential in terms of advancing radiology research and practice by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the heterogeneity of how these data are formatted. Indeed, although there is movement toward structured reporting in radiology (ie, hierarchically itemized reporting with use of standardized terminology), the majority of radiology reports remain unstructured and use free-form language. To effectively "mine" these large datasets for hypothesis testing, a robust strategy for extracting the necessary information is needed. Manual extraction of information is a time-consuming and often unmanageable task. "Intelligent" search engines that instead rely on natural language processing (NLP), a computer-based approach to analyzing free-form text or speech, can be used to automate this data mining task. The overall goal of NLP is to translate natural human language into a structured format (ie, a fixed collection of elements), each with a standardized set of choices for its value, that is easily manipulated by computer programs to (among other things) order into subcategories or query for the presence or absence of a finding. The authors review the fundamentals of NLP and describe various techniques that constitute NLP in radiology, along with some key applications. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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