A method of extracting the number of trial participants from abstracts describing randomized controlled trials.
We have developed a method for extracting the number of trial participants from abstracts describing randomized controlled trials (RCTs); the number of trial participants may be an indication of the reliability of the trial. The method depends on statistical natural language processing. The number o...
| Publicado en: | Journal of Telemedicine & Telecare Vol. 14; no. 7; pp. 354 - 359 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Sage Publications Inc.
Oct2008
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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=105581823&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105581823 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1357633X 8OL jtl: Journal of Telemedicine & Telecare issn: 1357633X maglogo: Y pubinfo: dt: Oct2008 vid: 14 iid: 7 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 105581823 2010111809 10.1258/jtt.2008.007007 NLM18852316 105581823 ppf: 354 ppct: 5 formats: tig: atl: A method of extracting the number of trial participants from abstracts describing randomized controlled trials. aug: au: Hansen MJ Rasmussen Nø Chung G affil: Aalborg University, Aalborg, Denmark. mjha03@hst.aau.dk sug: subj: Clinical Trials Information Retrieval Methods Natural Language Processing Patient Selection Algorithms Study Design Human ab: We have developed a method for extracting the number of trial participants from abstracts describing randomized controlled trials (RCTs); the number of trial participants may be an indication of the reliability of the trial. The method depends on statistical natural language processing. The number of interest was determined by a binary supervised classification based on a support vector machine algorithm. The method was trialled on 223 abstracts in which the number of trial participants was identified manually to act as a gold standard. Automatic extraction resulted in 2 false-positive and 19 false-negative classifications. The algorithm was capable of extracting the number of trial participants with an accuracy of 97% and an F-measure of 0.84. The algorithm may improve the selection of relevant articles in regard to question-answering, and hence may assist in decision-making. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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