| Intelligent analysis in predicting outcome of out-of-hospital cardiac arrest. | |
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MedLine Citation:
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PMID: 19342117 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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The prognosis among patients who suffer out-of-hospital cardiac arrest is poor. Higher survival rates have been observed only in patients with ventricular fibrillation who were fortunate enough to have basic and advanced life support initiated early after cardiac arrest. The ability to predict outcomes of cardiac arrest would be useful for resuscitation chains. Levels of EtCO(2)in expired air from lungs during cardiopulmonary resuscitation may serve as a non-invasive predictor of successful resuscitation and survival from cardiac arrest. Six different supervised learning classification techniques were used and evaluated. It has been shown that machine learning methods can provide an efficient way to detect important prognostic factors upon which further emergency unit actions are based. |
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Authors:
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Miljenko Krizmaric; Mateja Verlic; Gregor Stiglic; Stefek Grmec; Peter Kokol |
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Publication Detail:
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Type: Journal Article Date: 2009-04-01 |
Journal Detail:
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Title: Computer methods and programs in biomedicine Volume: 95 ISSN: 1872-7565 ISO Abbreviation: Comput Methods Programs Biomed Publication Date: 2009 Aug |
Date Detail:
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Created Date: 2009-07-20 Completed Date: 2009-11-10 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8506513 Medline TA: Comput Methods Programs Biomed Country: Ireland |
Other Details:
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Languages: eng Pagination: S22-32 Citation Subset: IM |
Affiliation:
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Faculty of Health Sciences, University of Maribor, Zitna 15, Maribor, Slovenia. miljenko.krizmaric@uni-mb.si |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Age Factors Artificial Intelligence* Heart Arrest / physiopathology*, therapy Humans Models, Theoretical* Outcome Assessment (Health Care)* Prognosis Sex Factors |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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