2012
DOI: 10.1186/1472-6947-12-41
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Text summarization as a decision support aid

Abstract: BackgroundPubMed data potentially can provide decision support information, but PubMed was not exclusively designed to be a point-of-care tool. Natural language processing applications that summarize PubMed citations hold promise for extracting decision support information. The objective of this study was to evaluate the efficiency of a text summarization application called Semantic MEDLINE, enhanced with a novel dynamic summarization method, in identifying decision support data.MethodsWe downloaded PubMed cit… Show more

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Cited by 10 publications
(7 citation statements)
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“…20 Studies on text summarization have demonstrated its utility in clinical information extraction and may serve as a potential decision support aid. 8,23 NLP could be applied to extract information from clinical encounters for text summarization and offer clinical decision support by helping identify a correct diagnosis. 8,23…”
Section: Clinical Decision Supportmentioning
confidence: 99%
“…20 Studies on text summarization have demonstrated its utility in clinical information extraction and may serve as a potential decision support aid. 8,23 NLP could be applied to extract information from clinical encounters for text summarization and offer clinical decision support by helping identify a correct diagnosis. 8,23…”
Section: Clinical Decision Supportmentioning
confidence: 99%
“…However, biomedical terminology is highly specialized and characterized by some peculiarities, such as lexical ambiguity and the frequent use of acronyms and abbreviations, thereby making automatic summarization different from that in other domains. Therefore, to substantially deal with these peculiarities, the most popular and effective approaches incorporated domain-specific knowledge sources, such as UMLS, in graph-based methods (Afantenos et al , 2005; Plaza et al , 2008; Workman et al , 2012; Zhang et al , 2013). The current study mainly investigates prior graph-based summarization research.…”
Section: Review Of the Literaturementioning
confidence: 99%
“…Workman and Hurdle presented the Combo algorithm to extract the genetic predicates for a particular disease, which outperformed a conventional summarization schema based on Semantic MEDLINE summarization in a genetic database curation [ 7 ]. Later, they proposed a novel dynamic summarization method in identifying decision support data [ 8 ]. Hristovski et al proposed an innovative methodology for biomedical QA, implemented as a search in the semantic database [ 9 ].…”
Section: Introductionmentioning
confidence: 99%